Overview

Brought to you by YData

Dataset statistics

Number of variables	34
Number of observations	19717
Missing cells	221379
Missing cells (%)	33.0%
Duplicate rows	157
Duplicate rows (%)	0.8%
Total size in memory	5.1 MiB
Average record size in memory	272.0 B

Variable types

Categorical	16
Text	18

Alerts

Dataset has 157 (0.8%) duplicate rows	Duplicates
`what_is_your_gender` is highly imbalanced (61.0%)	Imbalance
`what_programming_language_would_you_recommend_an_aspiring_data_scientist_to_learn_first` is highly imbalanced (65.0%)	Imbalance
`have_you_ever_used_a_tpu_tensor_processing_unit` is highly imbalanced (57.0%)	Imbalance
`what_is_the_highest_level_of_formal_education_that_you_have_attained_or_plan_to_attain_within_the_next_2_years` has 394 (2.0%) missing values	Missing
`select_the_title_most_similar_to_your_current_role_or_most_recent_title_if_retired` has 610 (3.1%) missing values	Missing
`what_is_the_size_of_the_company_where_you_are_employed` has 5715 (29.0%) missing values	Missing
`approximately_how_many_individuals_are_responsible_for_data_science_workloads_at_your_place_of_business` has 6094 (30.9%) missing values	Missing
`does_your_current_employer_incorporate_machine_learning_methods_into_their_business` has 6490 (32.9%) missing values	Missing
`what_is_your_current_yearly_compensation_approximate_$_usd` has 7220 (36.6%) missing values	Missing
`approximately_how_much_money_have_you_spent_on_machine_learning_and_or_cloud_computing_products_at_your_work_in_the_past_5_years` has 7467 (37.9%) missing values	Missing
`how_long_have_you_been_writing_code_to_analyze_data_at_work_or_at_school` has 4090 (20.7%) missing values	Missing
`what_programming_language_would_you_recommend_an_aspiring_data_scientist_to_learn_first` has 5340 (27.1%) missing values	Missing
`have_you_ever_used_a_tpu_tensor_processing_unit` has 5514 (28.0%) missing values	Missing
`for_how_many_years_have_you_used_machine_learning_methods` has 5535 (28.1%) missing values	Missing
`select_any_activities_that_make_up_an_important_part_of_your_role_at_work` has 10491 (53.2%) missing values	Missing
`who_what_are_your_favorite_media_sources_that_report_on_data_science_topics` has 2936 (14.9%) missing values	Missing
`on_which_platforms_have_you_begun_or_completed_data_science_courses` has 3148 (16.0%) missing values	Missing
`which_of_the_following_integrated_development_environments_id_es_do_you_use_on_a_regular_basis` has 5090 (25.8%) missing values	Missing
`which_of_the_following_hosted_notebook_products_do_you_use_on_a_regular_basis` has 5274 (26.7%) missing values	Missing
`what_programming_languages_do_you_use_on_a_regular_basis` has 5313 (26.9%) missing values	Missing
`what_data_visualization_libraries_or_tools_do_you_use_on_a_regular_basis` has 5464 (27.7%) missing values	Missing
`which_types_of_specialized_hardware_do_you_use_on_a_regular_basis` has 5499 (27.9%) missing values	Missing
`which_of_the_following_ml_algorithms_do_you_use_on_a_regular_basis` has 5629 (28.5%) missing values	Missing
`which_categories_of_ml_tools_do_you_use_on_a_regular_basis` has 5802 (29.4%) missing values	Missing
`which_categories_of_computer_vision_methods_do_you_use_on_a_regular_basis` has 14225 (72.1%) missing values	Missing
`which_of_the_following_natural_language_processing_nlp_methods_do_you_use_on_a_regular_basis` has 16135 (81.8%) missing values	Missing
`which_of_the_following_machine_learning_frameworks_do_you_use_on_a_regular_basis` has 5964 (30.2%) missing values	Missing
`which_of_the_following_cloud_computing_platforms_do_you_use_on_a_regular_basis` has 12592 (63.9%) missing values	Missing
`which_specific_cloud_computing_products_do_you_use_on_a_regular_basis` has 12617 (64.0%) missing values	Missing
`which_specific_big_data_analytics_products_do_you_use_on_a_regular_basis` has 12639 (64.1%) missing values	Missing
`which_of_the_following_machine_learning_products_do_you_use_on_a_regular_basis` has 12667 (64.2%) missing values	Missing
`which_automated_machine_learning_tools_or_partial_auto_ml_tools_do_you_use_on_a_regular_basis` has 12702 (64.4%) missing values	Missing
`which_of_the_following_relational_database_products_do_you_use_on_a_regular_basis` has 12723 (64.5%) missing values	Missing

Reproduction

Analysis started	2024-11-04 16:12:54.101887
Analysis finished	2024-11-04 16:13:18.026024
Duration	23.92 seconds
Software version	ydata-profiling vv4.12.0
Download configuration	config.json

what_is_your_age_#_years
Categorical

Distinct	11
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	154.2 KiB

25-29	4458
22-24	3610
30-34	3120
18-21	2502
35-39	2087
Other values (6)	3940

Length

Max length	5
Median length	5
Mean length	4.9898565
Min length	3

Characters and Unicode

Total characters	98385
Distinct characters	12
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	22-24
2nd row	40-44
3rd row	55-59
4th row	40-44
5th row	22-24

Common Values

Value	Count	Frequency (%)
25-29	4458	22.6%
22-24	3610	18.3%
30-34	3120	15.8%
18-21	2502	12.7%
35-39	2087	10.6%
40-44	1439	7.3%
45-49	949	4.8%
50-54	692	3.5%
55-59	422	2.1%
60-69	338	1.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
25-29	4458	22.6%
22-24	3610	18.3%
30-34	3120	15.8%
18-21	2502	12.7%
35-39	2087	10.6%
40-44	1439	7.3%
45-49	949	4.8%
50-54	692	3.5%
55-59	422	2.1%
60-69	338	1.7%

Most occurring characters

Value	Count	Frequency (%)
2	22248	22.6%
-	19617	19.9%
4	13637	13.9%
3	10414	10.6%
5	10144	10.3%
9	8254	8.4%
0	5689	5.8%
1	5004	5.1%
8	2502	2.5%
6	676	0.7%
Other values (2)	200	0.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	98385	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
2	22248	22.6%
-	19617	19.9%
4	13637	13.9%
3	10414	10.6%
5	10144	10.3%
9	8254	8.4%
0	5689	5.8%
1	5004	5.1%
8	2502	2.5%
6	676	0.7%
Other values (2)	200	0.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	98385	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
2	22248	22.6%
-	19617	19.9%
4	13637	13.9%
3	10414	10.6%
5	10144	10.3%
9	8254	8.4%
0	5689	5.8%
1	5004	5.1%
8	2502	2.5%
6	676	0.7%
Other values (2)	200	0.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	98385	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
2	22248	22.6%
-	19617	19.9%
4	13637	13.9%
3	10414	10.6%
5	10144	10.3%
9	8254	8.4%
0	5689	5.8%
1	5004	5.1%
8	2502	2.5%
6	676	0.7%
Other values (2)	200	0.2%

what_is_your_gender
Categorical

Imbalance

Distinct	4
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	154.2 KiB

Male	16138
Female	3212
Prefer not to say	318
Prefer to self-describe	49

Length

Max length	23
Median length	4
Mean length	4.5826951
Min length	4

Characters and Unicode

Total characters	90357
Distinct characters	20
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Male
2nd row	Male
3rd row	Female
4th row	Male
5th row	Male

Common Values

Value	Count	Frequency (%)
Male	16138	81.8%
Female	3212	16.3%
Prefer not to say	318	1.6%
Prefer to self-describe	49	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
male	16138	77.7%
female	3212	15.5%
prefer	367	1.8%
to	367	1.8%
not	318	1.5%
say	318	1.5%
self-describe	49	0.2%

Most occurring characters

Value	Count	Frequency (%)
e	23443	25.9%
a	19668	21.8%
l	19399	21.5%
M	16138	17.9%
F	3212	3.6%
m	3212	3.6%
	1052	1.2%
r	783	0.9%
o	685	0.8%
t	685	0.8%
Other values (10)	2080	2.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	90357	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	23443	25.9%
a	19668	21.8%
l	19399	21.5%
M	16138	17.9%
F	3212	3.6%
m	3212	3.6%
	1052	1.2%
r	783	0.9%
o	685	0.8%
t	685	0.8%
Other values (10)	2080	2.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	90357	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	23443	25.9%
a	19668	21.8%
l	19399	21.5%
M	16138	17.9%
F	3212	3.6%
m	3212	3.6%
	1052	1.2%
r	783	0.9%
o	685	0.8%
t	685	0.8%
Other values (10)	2080	2.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	90357	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	23443	25.9%
a	19668	21.8%
l	19399	21.5%
M	16138	17.9%
F	3212	3.6%
m	3212	3.6%
	1052	1.2%
r	783	0.9%
o	685	0.8%
t	685	0.8%
Other values (10)	2080	2.3%

in_which_country_do_you_currently_reside
Text

Distinct	59
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	154.2 KiB

Length

Max length	52
Median length	28
Mean length	10.232642
Min length	4

Characters and Unicode

Total characters	201757
Distinct characters	49
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	France
2nd row	India
3rd row	Germany
4th row	Australia
5th row	India

Value	Count	Frequency (%)
india	4786	14.3%
of	3736	11.2%
united	3567	10.6%
states	3085	9.2%
america	3085	9.2%
other	1054	3.1%
brazil	728	2.2%
japan	673	2.0%
russia	626	1.9%
china	574	1.7%
Other values (63)	11583	34.6%

Most occurring characters

Value	Count	Frequency (%)
a	24865	12.3%
i	19357	9.6%
e	17291	8.6%
n	16760	8.3%
t	14087	7.0%
	13780	6.8%
d	11371	5.6%
r	11349	5.6%
o	7043	3.5%
I	6091	3.0%
Other values (39)	59763	29.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	201757	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
a	24865	12.3%
i	19357	9.6%
e	17291	8.6%
n	16760	8.3%
t	14087	7.0%
	13780	6.8%
d	11371	5.6%
r	11349	5.6%
o	7043	3.5%
I	6091	3.0%
Other values (39)	59763	29.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	201757	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
a	24865	12.3%
i	19357	9.6%
e	17291	8.6%
n	16760	8.3%
t	14087	7.0%
	13780	6.8%
d	11371	5.6%
r	11349	5.6%
o	7043	3.5%
I	6091	3.0%
Other values (39)	59763	29.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	201757	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
a	24865	12.3%
i	19357	9.6%
e	17291	8.6%
n	16760	8.3%
t	14087	7.0%
	13780	6.8%
d	11371	5.6%
r	11349	5.6%
o	7043	3.5%
I	6091	3.0%
Other values (39)	59763	29.6%

what_is_the_highest_level_of_formal_education_that_you_have_attained_or_plan_to_attain_within_the_next_2_years
Categorical

Missing

Distinct	7
Distinct (%)	< 0.1%
Missing	394
Missing (%)	2.0%
Memory size	154.2 KiB

Master’s degree	8549
Bachelor’s degree	5993
Doctoral degree	2767
Some college/university study without earning a bachelor’s degree	837
Professional degree	611
Other values (2)	566

Length

Max length	65
Median length	15
Mean length	18.286446
Min length	15

Characters and Unicode

Total characters	353349
Distinct characters	31
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Master’s degree
2nd row	Professional degree
3rd row	Professional degree
4th row	Master’s degree
5th row	Bachelor’s degree

Common Values

Value	Count	Frequency (%)
Master’s degree	8549	43.4%
Bachelor’s degree	5993	30.4%
Doctoral degree	2767	14.0%
Some college/university study without earning a bachelor’s degree	837	4.2%
Professional degree	611	3.1%
I prefer not to answer	333	1.7%
No formal education past high school	233	1.2%
(Missing)	394	2.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
degree	18757	41.1%
master’s	8549	18.7%
bachelor’s	6830	15.0%
doctoral	2767	6.1%
some	837	1.8%
college/university	837	1.8%
study	837	1.8%
without	837	1.8%
earning	837	1.8%
a	837	1.8%
Other values (12)	3674	8.1%

Most occurring characters

Value	Count	Frequency (%)
e	77678	22.0%
r	40420	11.4%
s	27623	7.8%
	26276	7.4%
a	21463	6.1%
g	20664	5.8%
d	19827	5.6%
o	17928	5.1%
t	15796	4.5%
’	15379	4.4%
Other values (21)	70295	19.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	353349	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	77678	22.0%
r	40420	11.4%
s	27623	7.8%
	26276	7.4%
a	21463	6.1%
g	20664	5.8%
d	19827	5.6%
o	17928	5.1%
t	15796	4.5%
’	15379	4.4%
Other values (21)	70295	19.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	353349	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	77678	22.0%
r	40420	11.4%
s	27623	7.8%
	26276	7.4%
a	21463	6.1%
g	20664	5.8%
d	19827	5.6%
o	17928	5.1%
t	15796	4.5%
’	15379	4.4%
Other values (21)	70295	19.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	353349	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	77678	22.0%
r	40420	11.4%
s	27623	7.8%
	26276	7.4%
a	21463	6.1%
g	20664	5.8%
d	19827	5.6%
o	17928	5.1%
t	15796	4.5%
’	15379	4.4%
Other values (21)	70295	19.9%

select_the_title_most_similar_to_your_current_role_or_most_recent_title_if_retired
Categorical

Missing

Distinct	12
Distinct (%)	0.1%
Missing	610
Missing (%)	3.1%
Memory size	154.2 KiB

Data Scientist	4085
Student	4014
Software Engineer	2705
Other	1690
Data Analyst	1598
Other values (7)	5015

Length

Max length	23
Median length	18
Mean length	12.61276
Min length	5

Characters and Unicode

Total characters	240992
Distinct characters	33
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Software Engineer
2nd row	Software Engineer
3rd row	Other
4th row	Other
5th row	Data Scientist

Common Values

Value	Count	Frequency (%)
Data Scientist	4085	20.7%
Student	4014	20.4%
Software Engineer	2705	13.7%
Other	1690	8.6%
Data Analyst	1598	8.1%
Research Scientist	1470	7.5%
Not employed	942	4.8%
Business Analyst	778	3.9%
Product/Project Manager	723	3.7%
Data Engineer	624	3.2%
Other values (2)	478	2.4%
(Missing)	610	3.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
data	6307	19.6%
scientist	5555	17.3%
student	4014	12.5%
engineer	3485	10.8%
software	2705	8.4%
analyst	2376	7.4%
other	1690	5.3%
research	1470	4.6%
not	942	2.9%
employed	942	2.9%
Other values (5)	2702	8.4%

Most occurring characters

Value	Count	Frequency (%)
t	35726	14.8%
e	28138	11.7%
a	21723	9.0%
n	20738	8.6%
i	16339	6.8%
	13081	5.4%
S	12596	5.2%
s	12213	5.1%
r	11519	4.8%
c	8793	3.6%
Other values (23)	60126	24.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	240992	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
t	35726	14.8%
e	28138	11.7%
a	21723	9.0%
n	20738	8.6%
i	16339	6.8%
	13081	5.4%
S	12596	5.2%
s	12213	5.1%
r	11519	4.8%
c	8793	3.6%
Other values (23)	60126	24.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	240992	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
t	35726	14.8%
e	28138	11.7%
a	21723	9.0%
n	20738	8.6%
i	16339	6.8%
	13081	5.4%
S	12596	5.2%
s	12213	5.1%
r	11519	4.8%
c	8793	3.6%
Other values (23)	60126	24.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	240992	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
t	35726	14.8%
e	28138	11.7%
a	21723	9.0%
n	20738	8.6%
i	16339	6.8%
	13081	5.4%
S	12596	5.2%
s	12213	5.1%
r	11519	4.8%
c	8793	3.6%
Other values (23)	60126	24.9%

what_is_the_size_of_the_company_where_you_are_employed
Categorical

Missing

Distinct	5
Distinct (%)	< 0.1%
Missing	5715
Missing (%)	29.0%
Memory size	154.2 KiB

0-49 employees	4025
> 10,000 employees	3160
1000-9,999 employees	2641
50-249 employees	2329
250-999 employees	1847

Length

Max length	20
Median length	18
Mean length	16.76282
Min length	14

Characters and Unicode

Total characters	234713
Distinct characters	17
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1000-9,999 employees
2nd row	> 10,000 employees
3rd row	> 10,000 employees
4th row	0-49 employees
5th row	0-49 employees

Common Values

Value	Count	Frequency (%)
0-49 employees	4025	20.4%
> 10,000 employees	3160	16.0%
1000-9,999 employees	2641	13.4%
50-249 employees	2329	11.8%
250-999 employees	1847	9.4%
(Missing)	5715	29.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
employees	14002	44.9%
0-49	4025	12.9%
	3160	10.1%
10,000	3160	10.1%
1000-9,999	2641	8.5%
50-249	2329	7.5%
250-999	1847	5.9%

Most occurring characters

Value	Count	Frequency (%)
e	42006	17.9%
0	28764	12.3%
9	22459	9.6%
	17162	7.3%
o	14002	6.0%
s	14002	6.0%
y	14002	6.0%
l	14002	6.0%
p	14002	6.0%
m	14002	6.0%
Other values (7)	40310	17.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	234713	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	42006	17.9%
0	28764	12.3%
9	22459	9.6%
	17162	7.3%
o	14002	6.0%
s	14002	6.0%
y	14002	6.0%
l	14002	6.0%
p	14002	6.0%
m	14002	6.0%
Other values (7)	40310	17.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	234713	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	42006	17.9%
0	28764	12.3%
9	22459	9.6%
	17162	7.3%
o	14002	6.0%
s	14002	6.0%
y	14002	6.0%
l	14002	6.0%
p	14002	6.0%
m	14002	6.0%
Other values (7)	40310	17.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	234713	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	42006	17.9%
0	28764	12.3%
9	22459	9.6%
	17162	7.3%
o	14002	6.0%
s	14002	6.0%
y	14002	6.0%
l	14002	6.0%
p	14002	6.0%
m	14002	6.0%
Other values (7)	40310	17.2%

approximately_how_many_individuals_are_responsible_for_data_science_workloads_at_your_place_of_business
Categorical

Missing

Distinct	7
Distinct (%)	0.1%
Missing	6094
Missing (%)	30.9%
Memory size	154.2 KiB

20+	3178
1-2	3005
3-4	2319
0	1880
5-9	1847
Other values (2)	1394

Length

Max length	5
Median length	3
Mean length	2.9286501
Min length	1

Characters and Unicode

Total characters	39897
Distinct characters	9
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	20+
3rd row	20+
4th row	0
5th row	3-4

Common Values

Value	Count	Frequency (%)
20+	3178	16.1%
1-2	3005	15.2%
3-4	2319	11.8%
0	1880	9.5%
5-9	1847	9.4%
10-14	967	4.9%
15-19	427	2.2%
(Missing)	6094	30.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
20	3178	23.3%
1-2	3005	22.1%
3-4	2319	17.0%
0	1880	13.8%
5-9	1847	13.6%
10-14	967	7.1%
15-19	427	3.1%

Most occurring characters

Value	Count	Frequency (%)
-	8565	21.5%
2	6183	15.5%
0	6025	15.1%
1	5793	14.5%
4	3286	8.2%
+	3178	8.0%
3	2319	5.8%
5	2274	5.7%
9	2274	5.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	39897	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
-	8565	21.5%
2	6183	15.5%
0	6025	15.1%
1	5793	14.5%
4	3286	8.2%
+	3178	8.0%
3	2319	5.8%
5	2274	5.7%
9	2274	5.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	39897	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
-	8565	21.5%
2	6183	15.5%
0	6025	15.1%
1	5793	14.5%
4	3286	8.2%
+	3178	8.0%
3	2319	5.8%
5	2274	5.7%
9	2274	5.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	39897	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
-	8565	21.5%
2	6183	15.5%
0	6025	15.1%
1	5793	14.5%
4	3286	8.2%
+	3178	8.0%
3	2319	5.8%
5	2274	5.7%
9	2274	5.7%

does_your_current_employer_incorporate_machine_learning_methods_into_their_business
Categorical

Missing

Distinct	6
Distinct (%)	< 0.1%
Missing	6490
Missing (%)	32.9%
Memory size	154.2 KiB

We are exploring ML methods (and may one day put a model into production)	2812
We recently started using ML methods (i.e., models in production for less than 2 years)	2731
We have well established ML methods (i.e., models in production for more than 2 years)	2528
No (we do not use ML methods)	2415
We use ML methods for generating insights (but do not put working models into production)	1550

Length

Max length	89
Median length	86
Mean length	66.814017
Min length	13

Characters and Unicode

Total characters	883749
Distinct characters	34
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	I do not know
2nd row	We have well established ML methods (i.e., models in production for more than 2 years)
3rd row	I do not know
4th row	No (we do not use ML methods)
5th row	We have well established ML methods (i.e., models in production for more than 2 years)

Common Values

Value	Count	Frequency (%)
We are exploring ML methods (and may one day put a model into production)	2812	14.3%
We recently started using ML methods (i.e., models in production for less than 2 years)	2731	13.9%
We have well established ML methods (i.e., models in production for more than 2 years)	2528	12.8%
No (we do not use ML methods)	2415	12.2%
We use ML methods for generating insights (but do not put working models into production)	1550	7.9%
I do not know	1191	6.0%
(Missing)	6490	32.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
we	12036	7.4%
ml	12036	7.4%
methods	12036	7.4%
production	9621	5.9%
for	6809	4.2%
models	6809	4.2%
years	5259	3.2%
2	5259	3.2%
than	5259	3.2%
i.e	5259	3.2%
Other values (29)	82789	50.7%

Most occurring characters

Value	Count	Frequency (%)
	149945	17.0%
e	83276	9.4%
o	75690	8.6%
t	56167	6.4%
n	50946	5.8%
d	47317	5.4%
s	47149	5.3%
i	38772	4.4%
r	38403	4.3%
a	33915	3.8%
Other values (24)	262169	29.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	883749	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	149945	17.0%
e	83276	9.4%
o	75690	8.6%
t	56167	6.4%
n	50946	5.8%
d	47317	5.4%
s	47149	5.3%
i	38772	4.4%
r	38403	4.3%
a	33915	3.8%
Other values (24)	262169	29.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	883749	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	149945	17.0%
e	83276	9.4%
o	75690	8.6%
t	56167	6.4%
n	50946	5.8%
d	47317	5.4%
s	47149	5.3%
i	38772	4.4%
r	38403	4.3%
a	33915	3.8%
Other values (24)	262169	29.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	883749	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	149945	17.0%
e	83276	9.4%
o	75690	8.6%
t	56167	6.4%
n	50946	5.8%
d	47317	5.4%
s	47149	5.3%
i	38772	4.4%
r	38403	4.3%
a	33915	3.8%
Other values (24)	262169	29.7%

what_is_your_current_yearly_compensation_approximate_$_usd
Categorical

Missing

Distinct	25
Distinct (%)	0.2%
Missing	7220
Missing (%)	36.6%
Memory size	154.2 KiB

$0-999	1513
10,000-14,999	833
100,000-124,999	750
30,000-39,999	728
40,000-49,999	719
Other values (20)	7954

Length

Max length	15
Median length	13
Mean length	12.04361
Min length	6

Characters and Unicode

Total characters	150509
Distinct characters	15
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	30,000-39,999
2nd row	5,000-7,499
3rd row	250,000-299,999
4th row	4,000-4,999
5th row	60,000-69,999

Common Values

Value	Count	Frequency (%)
$0-999	1513	7.7%
10,000-14,999	833	4.2%
100,000-124,999	750	3.8%
30,000-39,999	728	3.7%
40,000-49,999	719	3.6%
50,000-59,999	704	3.6%
1,000-1,999	599	3.0%
60,000-69,999	576	2.9%
5,000-7,499	536	2.7%
15,000-19,999	529	2.7%
Other values (15)	5010	25.4%
(Missing)	7220	36.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
0-999	1513	12.0%
10,000-14,999	833	6.6%
100,000-124,999	750	6.0%
30,000-39,999	728	5.8%
40,000-49,999	719	5.7%
50,000-59,999	704	5.6%
1,000-1,999	599	4.8%
60,000-69,999	576	4.6%
5,000-7,499	536	4.3%
15,000-19,999	529	4.2%
Other values (16)	5093	40.5%

Most occurring characters

Value	Count	Frequency (%)
9	44336	29.5%
0	42462	28.2%
,	21885	14.5%
-	12414	8.2%
1	7256	4.8%
4	5309	3.5%
5	4502	3.0%
2	4489	3.0%
3	2140	1.4%
7	1992	1.3%
Other values (5)	3724	2.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	150509	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
9	44336	29.5%
0	42462	28.2%
,	21885	14.5%
-	12414	8.2%
1	7256	4.8%
4	5309	3.5%
5	4502	3.0%
2	4489	3.0%
3	2140	1.4%
7	1992	1.3%
Other values (5)	3724	2.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	150509	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
9	44336	29.5%
0	42462	28.2%
,	21885	14.5%
-	12414	8.2%
1	7256	4.8%
4	5309	3.5%
5	4502	3.0%
2	4489	3.0%
3	2140	1.4%
7	1992	1.3%
Other values (5)	3724	2.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	150509	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
9	44336	29.5%
0	42462	28.2%
,	21885	14.5%
-	12414	8.2%
1	7256	4.8%
4	5309	3.5%
5	4502	3.0%
2	4489	3.0%
3	2140	1.4%
7	1992	1.3%
Other values (5)	3724	2.5%

approximately_how_much_money_have_you_spent_on_machine_learning_and_or_cloud_computing_products_at_your_work_in_the_past_5_years
Categorical

Missing

Distinct	6
Distinct (%)	< 0.1%
Missing	7467
Missing (%)	37.9%
Memory size	154.2 KiB

$0 (USD)	4038
$100-$999	2335
$1000-$9,999	2123
$1-$99	1485
$10,000-$99,999	1268

Length

Max length	17
Median length	15
Mean length	10.101388
Min length	6

Characters and Unicode

Total characters	123742
Distinct characters	13
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	$0 (USD)
2nd row	> $100,000 ($USD)
3rd row	$10,000-$99,999
4th row	$0 (USD)
5th row	$10,000-$99,999

Common Values

Value	Count	Frequency (%)
$0 (USD)	4038	20.5%
$100-$999	2335	11.8%
$1000-$9,999	2123	10.8%
$1-$99	1485	7.5%
$10,000-$99,999	1268	6.4%
> $100,000 ($USD)	1001	5.1%
(Missing)	7467	37.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
usd	5039	27.6%
0	4038	22.1%
100-$999	2335	12.8%
1000-$9,999	2123	11.6%
1-$99	1485	8.1%
10,000-$99,999	1268	6.9%
	1001	5.5%
100,000	1001	5.5%

Most occurring characters

Value	Count	Frequency (%)
0	25154	20.3%
9	24807	20.0%
$	20462	16.5%
1	8212	6.6%
-	7211	5.8%
	6040	4.9%
,	5660	4.6%
(	5039	4.1%
U	5039	4.1%
S	5039	4.1%
Other values (3)	11079	9.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	123742	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	25154	20.3%
9	24807	20.0%
$	20462	16.5%
1	8212	6.6%
-	7211	5.8%
	6040	4.9%
,	5660	4.6%
(	5039	4.1%
U	5039	4.1%
S	5039	4.1%
Other values (3)	11079	9.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	123742	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	25154	20.3%
9	24807	20.0%
$	20462	16.5%
1	8212	6.6%
-	7211	5.8%
	6040	4.9%
,	5660	4.6%
(	5039	4.1%
U	5039	4.1%
S	5039	4.1%
Other values (3)	11079	9.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	123742	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	25154	20.3%
9	24807	20.0%
$	20462	16.5%
1	8212	6.6%
-	7211	5.8%
	6040	4.9%
,	5660	4.6%
(	5039	4.1%
U	5039	4.1%
S	5039	4.1%
Other values (3)	11079	9.0%

what_is_the_primary_tool_that_you_use_at_work_or_school_to_analyze_data
Text

Distinct	4975
Distinct (%)	25.2%
Missing	0
Missing (%)	0.0%
Memory size	154.2 KiB

Length

Max length	89
Median length	86
Mean length	63.65938
Min length	18

Characters and Unicode

Total characters	1255172
Distinct characters	55
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4355 ?
Unique (%)	22.1%

Sample

1st row	Basic statistical software (Microsoft Excel, Google Sheets, etc.), 0, -1, -1, -1, -1
2nd row	Cloud-based data software & APIs (AWS, GCP, Azure, etc.), -1, -1, -1, -1, 0
3rd row	-1, -1, -1, -1, -1
4th row	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 0, -1
5th row	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 1, -1

Value	Count	Frequency (%)
1	85278	43.2%
etc	14500	7.3%
local	8475	4.3%
development	8475	4.3%
environments	8475	4.3%
rstudio	8475	4.3%
jupyterlab	8475	4.3%
software	6025	3.1%
statistical	3956	2.0%
excel	3061	1.6%
Other values (2861)	42147	21.4%

Most occurring characters

Value	Count	Frequency (%)
	177625	14.2%
,	125627	10.0%
e	95128	7.6%
1	90608	7.2%
-	85279	6.8%
t	76555	6.1%
o	55119	4.4%
a	41050	3.3%
c	38771	3.1%
s	37495	3.0%
Other values (45)	431915	34.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1255172	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	177625	14.2%
,	125627	10.0%
e	95128	7.6%
1	90608	7.2%
-	85279	6.8%
t	76555	6.1%
o	55119	4.4%
a	41050	3.3%
c	38771	3.1%
s	37495	3.0%
Other values (45)	431915	34.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1255172	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	177625	14.2%
,	125627	10.0%
e	95128	7.6%
1	90608	7.2%
-	85279	6.8%
t	76555	6.1%
o	55119	4.4%
a	41050	3.3%
c	38771	3.1%
s	37495	3.0%
Other values (45)	431915	34.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1255172	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	177625	14.2%
,	125627	10.0%
e	95128	7.6%
1	90608	7.2%
-	85279	6.8%
t	76555	6.1%
o	55119	4.4%
a	41050	3.3%
c	38771	3.1%
s	37495	3.0%
Other values (45)	431915	34.4%

how_long_have_you_been_writing_code_to_analyze_data_at_work_or_at_school
Categorical

Missing

Distinct	7
Distinct (%)	< 0.1%
Missing	4090
Missing (%)	20.7%
Memory size	154.2 KiB

1-2 years	4061
< 1 years	3828
3-5 years	3365
5-10 years	1887
10-20 years	1045
Other values (2)	1441

Length

Max length	25
Median length	9
Mean length	10.140142
Min length	9

Characters and Unicode

Total characters	158460
Distinct characters	24
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1-2 years
2nd row	I have never written code
3rd row	1-2 years
4th row	< 1 years
5th row	20+ years

Common Values

Value	Count	Frequency (%)
1-2 years	4061	20.6%
< 1 years	3828	19.4%
3-5 years	3365	17.1%
5-10 years	1887	9.6%
10-20 years	1045	5.3%
I have never written code	865	4.4%
20+ years	576	2.9%
(Missing)	4090	20.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
years	14762	39.2%
1-2	4061	10.8%
	3828	10.2%
1	3828	10.2%
3-5	3365	8.9%
5-10	1887	5.0%
10-20	1045	2.8%
i	865	2.3%
have	865	2.3%
never	865	2.3%
Other values (3)	2306	6.1%

Most occurring characters

Value	Count	Frequency (%)
	22050	13.9%
e	19087	12.0%
r	16492	10.4%
a	15627	9.9%
y	14762	9.3%
s	14762	9.3%
1	10821	6.8%
-	10358	6.5%
2	5682	3.6%
5	5252	3.3%
Other values (14)	23567	14.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	158460	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	22050	13.9%
e	19087	12.0%
r	16492	10.4%
a	15627	9.9%
y	14762	9.3%
s	14762	9.3%
1	10821	6.8%
-	10358	6.5%
2	5682	3.6%
5	5252	3.3%
Other values (14)	23567	14.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	158460	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	22050	13.9%
e	19087	12.0%
r	16492	10.4%
a	15627	9.9%
y	14762	9.3%
s	14762	9.3%
1	10821	6.8%
-	10358	6.5%
2	5682	3.6%
5	5252	3.3%
Other values (14)	23567	14.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	158460	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	22050	13.9%
e	19087	12.0%
r	16492	10.4%
a	15627	9.9%
y	14762	9.3%
s	14762	9.3%
1	10821	6.8%
-	10358	6.5%
2	5682	3.6%
5	5252	3.3%
Other values (14)	23567	14.9%

what_programming_language_would_you_recommend_an_aspiring_data_scientist_to_learn_first
Categorical

Imbalance Missing

Distinct	12
Distinct (%)	0.1%
Missing	5340
Missing (%)	27.1%
Memory size	154.2 KiB

Python	11316
R	1343
SQL	817
C++	199
MATLAB	162
Other values (7)	540

Length

Max length	10
Median length	6
Mean length	5.2444182
Min length	1

Characters and Unicode

Total characters	75399
Distinct characters	27
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Python
2nd row	Python
3rd row	Python
4th row	Java
5th row	Python

Common Values

Value	Count	Frequency (%)
Python	11316	57.4%
R	1343	6.8%
SQL	817	4.1%
C++	199	1.0%
MATLAB	162	0.8%
C	153	0.8%
Other	127	0.6%
Java	104	0.5%
None	69	0.3%
Javascript	47	0.2%
Other values (2)	40	0.2%
(Missing)	5340	27.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
python	11316	78.7%
r	1343	9.3%
sql	817	5.7%
c	352	2.4%
matlab	162	1.1%
other	127	0.9%
java	104	0.7%
none	69	0.5%
javascript	47	0.3%
bash	35	0.2%

Most occurring characters

Value	Count	Frequency (%)
t	11495	15.2%
h	11478	15.2%
o	11385	15.1%
n	11385	15.1%
y	11321	15.0%
P	11316	15.0%
R	1343	1.8%
L	979	1.3%
S	822	1.1%
Q	817	1.1%
Other values (17)	3058	4.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	75399	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
t	11495	15.2%
h	11478	15.2%
o	11385	15.1%
n	11385	15.1%
y	11321	15.0%
P	11316	15.0%
R	1343	1.8%
L	979	1.3%
S	822	1.1%
Q	817	1.1%
Other values (17)	3058	4.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	75399	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
t	11495	15.2%
h	11478	15.2%
o	11385	15.1%
n	11385	15.1%
y	11321	15.0%
P	11316	15.0%
R	1343	1.8%
L	979	1.3%
S	822	1.1%
Q	817	1.1%
Other values (17)	3058	4.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	75399	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
t	11495	15.2%
h	11478	15.2%
o	11385	15.1%
n	11385	15.1%
y	11321	15.0%
P	11316	15.0%
R	1343	1.8%
L	979	1.3%
S	822	1.1%
Q	817	1.1%
Other values (17)	3058	4.1%

have_you_ever_used_a_tpu_tensor_processing_unit
Categorical

Imbalance Missing

Distinct	5
Distinct (%)	< 0.1%
Missing	5514
Missing (%)	28.0%
Memory size	154.2 KiB

Never	11495
Once	1320
2-5 times	1037
6-24 times	193
> 25 times	158

Length

Max length	10
Median length	5
Mean length	5.3226783
Min length	4

Characters and Unicode

Total characters	75598
Distinct characters	18
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Never
2nd row	Once
3rd row	Never
4th row	Never
5th row	6-24 times

Common Values

Value	Count	Frequency (%)
Never	11495	58.3%
Once	1320	6.7%
2-5 times	1037	5.3%
6-24 times	193	1.0%
> 25 times	158	0.8%
(Missing)	5514	28.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
never	11495	73.0%
times	1388	8.8%
once	1320	8.4%
2-5	1037	6.6%
6-24	193	1.2%
	158	1.0%
25	158	1.0%

Most occurring characters

Value	Count	Frequency (%)
e	25698	34.0%
N	11495	15.2%
v	11495	15.2%
r	11495	15.2%
	1546	2.0%
s	1388	1.8%
2	1388	1.8%
t	1388	1.8%
i	1388	1.8%
m	1388	1.8%
Other values (8)	6929	9.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	75598	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	25698	34.0%
N	11495	15.2%
v	11495	15.2%
r	11495	15.2%
	1546	2.0%
s	1388	1.8%
2	1388	1.8%
t	1388	1.8%
i	1388	1.8%
m	1388	1.8%
Other values (8)	6929	9.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	75598	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	25698	34.0%
N	11495	15.2%
v	11495	15.2%
r	11495	15.2%
	1546	2.0%
s	1388	1.8%
2	1388	1.8%
t	1388	1.8%
i	1388	1.8%
m	1388	1.8%
Other values (8)	6929	9.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	75598	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	25698	34.0%
N	11495	15.2%
v	11495	15.2%
r	11495	15.2%
	1546	2.0%
s	1388	1.8%
2	1388	1.8%
t	1388	1.8%
i	1388	1.8%
m	1388	1.8%
Other values (8)	6929	9.2%

for_how_many_years_have_you_used_machine_learning_methods
Categorical

Missing

Distinct	8
Distinct (%)	0.1%
Missing	5535
Missing (%)	28.1%
Memory size	154.2 KiB

< 1 years	5149
1-2 years	3798
2-3 years	1840
3-4 years	1080
4-5 years	927
Other values (3)	1388

Length

Max length	11
Median length	9
Mean length	9.1086589
Min length	9

Characters and Unicode

Total characters	129179
Distinct characters	15
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1-2 years
2nd row	2-3 years
3rd row	< 1 years
4th row	10-15 years
5th row	2-3 years

Common Values

Value	Count	Frequency (%)
< 1 years	5149	26.1%
1-2 years	3798	19.3%
2-3 years	1840	9.3%
3-4 years	1080	5.5%
4-5 years	927	4.7%
5-10 years	869	4.4%
10-15 years	336	1.7%
20+ years	183	0.9%
(Missing)	5535	28.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
years	14182	42.3%
	5149	15.4%
1	5149	15.4%
1-2	3798	11.3%
2-3	1840	5.5%
3-4	1080	3.2%
4-5	927	2.8%
5-10	869	2.6%
10-15	336	1.0%
20	183	0.5%

Most occurring characters

Value	Count	Frequency (%)
	19331	15.0%
y	14182	11.0%
e	14182	11.0%
a	14182	11.0%
r	14182	11.0%
s	14182	11.0%
1	10488	8.1%
-	8850	6.9%
2	5821	4.5%
<	5149	4.0%
Other values (5)	8630	6.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	129179	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	19331	15.0%
y	14182	11.0%
e	14182	11.0%
a	14182	11.0%
r	14182	11.0%
s	14182	11.0%
1	10488	8.1%
-	8850	6.9%
2	5821	4.5%
<	5149	4.0%
Other values (5)	8630	6.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	129179	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	19331	15.0%
y	14182	11.0%
e	14182	11.0%
a	14182	11.0%
r	14182	11.0%
s	14182	11.0%
1	10488	8.1%
-	8850	6.9%
2	5821	4.5%
<	5149	4.0%
Other values (5)	8630	6.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	129179	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	19331	15.0%
y	14182	11.0%
e	14182	11.0%
a	14182	11.0%
r	14182	11.0%
s	14182	11.0%
1	10488	8.1%
-	8850	6.9%
2	5821	4.5%
<	5149	4.0%
Other values (5)	8630	6.7%

select_any_activities_that_make_up_an_important_part_of_your_role_at_work
Text

Missing

Distinct	98
Distinct (%)	1.1%
Missing	10491
Missing (%)	53.2%
Memory size	154.2 KiB

Length

Max length	485
Median length	364
Mean length	207.43822
Min length	5

Characters and Unicode

Total characters	1913825
Distinct characters	35
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	13 ?
Unique (%)	0.1%

Sample

1st row	Analyze and understand data to influence product or business decisions, Build and/or run the data infrastructure that my business uses for storing, analyzing, and operationalizing data, Build prototypes to explore applying machine learning to new areas, Build and/or run a machine learning service that operationally improves my product or workflows
2nd row	Build prototypes to explore applying machine learning to new areas, Do research that advances the state of the art of machine learning
3rd row	Analyze and understand data to influence product or business decisions, Experimentation and iteration to improve existing ML models, Do research that advances the state of the art of machine learning
4th row	Analyze and understand data to influence product or business decisions, Build prototypes to explore applying machine learning to new areas, Build and/or run a machine learning service that operationally improves my product or workflows
5th row	Other

Value	Count	Frequency (%)
to	19758	7.1%
and	13362	4.8%
data	13223	4.7%
build	11895	4.3%
machine	10688	3.8%
learning	10688	3.8%
business	9657	3.5%
product	9439	3.4%
or	9439	3.4%
that	9273	3.3%
Other values (47)	162326	58.0%

Most occurring characters

Value	Count	Frequency (%)
	270522	14.1%
n	158935	8.3%
a	154761	8.1%
e	153868	8.0%
t	132483	6.9%
i	125681	6.6%
o	122671	6.4%
r	120312	6.3%
s	97063	5.1%
d	79170	4.1%
Other values (25)	498359	26.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1913825	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	270522	14.1%
n	158935	8.3%
a	154761	8.1%
e	153868	8.0%
t	132483	6.9%
i	125681	6.6%
o	122671	6.4%
r	120312	6.3%
s	97063	5.1%
d	79170	4.1%
Other values (25)	498359	26.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1913825	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	270522	14.1%
n	158935	8.3%
a	154761	8.1%
e	153868	8.0%
t	132483	6.9%
i	125681	6.6%
o	122671	6.4%
r	120312	6.3%
s	97063	5.1%
d	79170	4.1%
Other values (25)	498359	26.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1913825	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	270522	14.1%
n	158935	8.3%
a	154761	8.1%
e	153868	8.0%
t	132483	6.9%
i	125681	6.6%
o	122671	6.4%
r	120312	6.3%
s	97063	5.1%
d	79170	4.1%
Other values (25)	498359	26.0%

who_what_are_your_favorite_media_sources_that_report_on_data_science_topics
Text

Missing

Distinct	1020
Distinct (%)	6.1%
Missing	2936
Missing (%)	14.9%
Memory size	154.2 KiB

Length

Max length	512
Median length	412
Mean length	153.9391
Min length	4

Characters and Unicode

Total characters	2583252
Distinct characters	49
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	313 ?
Unique (%)	1.9%

Sample

1st row	Twitter (data science influencers), Kaggle (forums, blog, social media, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)
2nd row	Kaggle (forums, blog, social media, etc), YouTube (Cloud AI Adventures, Siraj Raval, etc), Podcasts (Chai Time Data Science, Linear Digressions, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc)
3rd row	Podcasts (Chai Time Data Science, Linear Digressions, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc), Slack Communities (ods.ai, kagglenoobs, etc)
4th row	YouTube (Cloud AI Adventures, Siraj Raval, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Other
5th row	YouTube (Cloud AI Adventures, Siraj Raval, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)

Value	Count	Frequency (%)
etc	44329	14.0%
data	15722	5.0%
science	15722	5.0%
forums	14506	4.6%
kaggle	10751	3.4%
blog	10751	3.4%
social	10751	3.4%
media	10751	3.4%
kdnuggets	9907	3.1%
vidhya	9907	3.1%
Other values (36)	164129	51.7%

Most occurring characters

Value	Count	Frequency (%)
	300445	11.6%
e	194465	7.5%
a	181119	7.0%
i	150113	5.8%
,	140412	5.4%
t	138919	5.4%
s	131026	5.1%
c	129271	5.0%
o	120553	4.7%
n	104219	4.0%
Other values (39)	992710	38.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2583252	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	300445	11.6%
e	194465	7.5%
a	181119	7.0%
i	150113	5.8%
,	140412	5.4%
t	138919	5.4%
s	131026	5.1%
c	129271	5.0%
o	120553	4.7%
n	104219	4.0%
Other values (39)	992710	38.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2583252	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	300445	11.6%
e	194465	7.5%
a	181119	7.0%
i	150113	5.8%
,	140412	5.4%
t	138919	5.4%
s	131026	5.1%
c	129271	5.0%
o	120553	4.7%
n	104219	4.0%
Other values (39)	992710	38.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2583252	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	300445	11.6%
e	194465	7.5%
a	181119	7.0%
i	150113	5.8%
,	140412	5.4%
t	138919	5.4%
s	131026	5.1%
c	129271	5.0%
o	120553	4.7%
n	104219	4.0%
Other values (39)	992710	38.4%

on_which_platforms_have_you_begun_or_completed_data_science_courses
Text

Missing

Distinct	819
Distinct (%)	4.9%
Missing	3148
Missing (%)	16.0%
Memory size	154.2 KiB

Length

Max length	176
Median length	151
Mean length	40.252942
Min length	3

Characters and Unicode

Total characters	666951
Distinct characters	35
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	262 ?
Unique (%)	1.6%

Sample

1st row	Coursera, DataCamp, Kaggle Courses (i.e. Kaggle Learn), Udemy
2nd row	Coursera, DataCamp, Kaggle Courses (i.e. Kaggle Learn), Udemy
3rd row	Coursera, edX, DataCamp, University Courses (resulting in a university degree)
4th row	Other
5th row	None

Value	Count	Frequency (%)
kaggle	10238	11.6%
courses	9597	10.8%
university	8956	10.1%
coursera	8685	9.8%
i.e	5119	5.8%
learn	5119	5.8%
udemy	4804	5.4%
resulting	4478	5.1%
in	4478	5.1%
a	4478	5.1%
Other values (10)	22616	25.5%

Most occurring characters

Value	Count	Frequency (%)
e	80252	12.0%
	71999	10.8%
r	53153	8.0%
a	48811	7.3%
s	43576	6.5%
i	39026	5.9%
g	30715	4.6%
n	29654	4.4%
u	27981	4.2%
t	25108	3.8%
Other values (25)	216676	32.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	666951	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	80252	12.0%
	71999	10.8%
r	53153	8.0%
a	48811	7.3%
s	43576	6.5%
i	39026	5.9%
g	30715	4.6%
n	29654	4.4%
u	27981	4.2%
t	25108	3.8%
Other values (25)	216676	32.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	666951	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	80252	12.0%
	71999	10.8%
r	53153	8.0%
a	48811	7.3%
s	43576	6.5%
i	39026	5.9%
g	30715	4.6%
n	29654	4.4%
u	27981	4.2%
t	25108	3.8%
Other values (25)	216676	32.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	666951	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	80252	12.0%
	71999	10.8%
r	53153	8.0%
a	48811	7.3%
s	43576	6.5%
i	39026	5.9%
g	30715	4.6%
n	29654	4.4%
u	27981	4.2%
t	25108	3.8%
Other values (25)	216676	32.5%

which_of_the_following_integrated_development_environments_id_es_do_you_use_on_a_regular_basis
Text

Missing

Distinct	853
Distinct (%)	5.8%
Missing	5090
Missing (%)	25.8%
Memory size	154.2 KiB

Length

Max length	185
Median length	160
Mean length	64.706023
Min length	4

Characters and Unicode

Total characters	946455
Distinct characters	39
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	274 ?
Unique (%)	1.9%

Sample

1st row	Jupyter (JupyterLab, Jupyter Notebooks, etc) , RStudio , PyCharm , MATLAB , Spyder
2nd row	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Visual Studio / Visual Studio Code
3rd row	Jupyter (JupyterLab, Jupyter Notebooks, etc)
4th row	RStudio , Other
5th row	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Spyder , Notepad++ , Sublime Text

Value	Count	Frequency (%)
	30693	22.7%
jupyter	21608	15.9%
notebooks	10804	8.0%
etc	10804	8.0%
jupyterlab	10804	8.0%
visual	9068	6.7%
studio	9068	6.7%
code	4534	3.3%
rstudio	4455	3.3%
pycharm	4224	3.1%
Other values (10)	19424	14.3%

Most occurring characters

Value	Count	Frequency (%)
	183434	19.4%
t	75657	8.0%
e	71159	7.5%
u	57658	6.1%
o	55475	5.9%
,	45985	4.9%
r	40412	4.3%
y	39721	4.2%
p	38778	4.1%
J	32412	3.4%
Other values (29)	305764	32.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	946455	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	183434	19.4%
t	75657	8.0%
e	71159	7.5%
u	57658	6.1%
o	55475	5.9%
,	45985	4.9%
r	40412	4.3%
y	39721	4.2%
p	38778	4.1%
J	32412	3.4%
Other values (29)	305764	32.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	946455	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	183434	19.4%
t	75657	8.0%
e	71159	7.5%
u	57658	6.1%
o	55475	5.9%
,	45985	4.9%
r	40412	4.3%
y	39721	4.2%
p	38778	4.1%
J	32412	3.4%
Other values (29)	305764	32.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	946455	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	183434	19.4%
t	75657	8.0%
e	71159	7.5%
u	57658	6.1%
o	55475	5.9%
,	45985	4.9%
r	40412	4.3%
y	39721	4.2%
p	38778	4.1%
J	32412	3.4%
Other values (29)	305764	32.3%

which_of_the_following_hosted_notebook_products_do_you_use_on_a_regular_basis
Text

Missing

Distinct	248
Distinct (%)	1.7%
Missing	5274
Missing (%)	26.7%
Memory size	154.2 KiB

Length

Max length	295
Median length	254
Mean length	29.514851
Min length	4

Characters and Unicode

Total characters	426283
Distinct characters	44
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	100 ?
Unique (%)	0.7%

Sample

1st row	None
2nd row	Microsoft Azure Notebooks
3rd row	Google Colab , Google Cloud Notebook Products (AI Platform, Datalab, etc)
4th row	None
5th row	Kaggle Notebooks (Kernels) , Google Colab , Binder / JupyterHub

Value	Count	Frequency (%)
	7815	12.9%
notebooks	7214	11.9%
google	5672	9.4%
none	5177	8.5%
kernels	4845	8.0%
kaggle	4845	8.0%
colab	4551	7.5%
products	1878	3.1%
etc	1878	3.1%
notebook	1878	3.1%
Other values (20)	14831	24.5%

Most occurring characters

Value	Count	Frequency (%)
	68912	16.2%
o	55693	13.1%
e	43123	10.1%
l	23377	5.5%
t	19566	4.6%
a	16565	3.9%
b	16546	3.9%
g	16119	3.8%
s	15603	3.7%
r	14417	3.4%
Other values (34)	136362	32.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	426283	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	68912	16.2%
o	55693	13.1%
e	43123	10.1%
l	23377	5.5%
t	19566	4.6%
a	16565	3.9%
b	16546	3.9%
g	16119	3.8%
s	15603	3.7%
r	14417	3.4%
Other values (34)	136362	32.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	426283	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	68912	16.2%
o	55693	13.1%
e	43123	10.1%
l	23377	5.5%
t	19566	4.6%
a	16565	3.9%
b	16546	3.9%
g	16119	3.8%
s	15603	3.7%
r	14417	3.4%
Other values (34)	136362	32.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	426283	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	68912	16.2%
o	55693	13.1%
e	43123	10.1%
l	23377	5.5%
t	19566	4.6%
a	16565	3.9%
b	16546	3.9%
g	16119	3.8%
s	15603	3.7%
r	14417	3.4%
Other values (34)	136362	32.0%

what_programming_languages_do_you_use_on_a_regular_basis
Text

Missing

Distinct	611
Distinct (%)	4.2%
Missing	5313
Missing (%)	26.9%
Memory size	154.2 KiB

Length

Max length	70
Median length	60
Mean length	14.848792
Min length	1

Characters and Unicode

Total characters	213882
Distinct characters	29
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	215 ?
Unique (%)	1.5%

Sample

1st row	Python, R, SQL, Java, Javascript, MATLAB
2nd row	Python, R, SQL, Bash
3rd row	Python, SQL
4th row	Python, R
5th row	Python, R, Bash

Value	Count	Frequency (%)
python	12841	34.2%
sql	6532	17.4%
r	4588	12.2%
c	3928	10.5%
java	2267	6.0%
javascript	2174	5.8%
bash	2037	5.4%
matlab	1516	4.0%
other	1148	3.1%
typescript	389	1.0%

Most occurring characters

Value	Count	Frequency (%)
,	23099	10.8%
	23099	10.8%
t	16552	7.7%
h	16026	7.5%
y	13230	6.2%
o	12924	6.0%
n	12924	6.0%
P	12841	6.0%
a	10919	5.1%
L	8048	3.8%
Other values (19)	64220	30.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	213882	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
,	23099	10.8%
	23099	10.8%
t	16552	7.7%
h	16026	7.5%
y	13230	6.2%
o	12924	6.0%
n	12924	6.0%
P	12841	6.0%
a	10919	5.1%
L	8048	3.8%
Other values (19)	64220	30.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	213882	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
,	23099	10.8%
	23099	10.8%
t	16552	7.7%
h	16026	7.5%
y	13230	6.2%
o	12924	6.0%
n	12924	6.0%
P	12841	6.0%
a	10919	5.1%
L	8048	3.8%
Other values (19)	64220	30.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	213882	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
,	23099	10.8%
	23099	10.8%
t	16552	7.7%
h	16026	7.5%
y	13230	6.2%
o	12924	6.0%
n	12924	6.0%
P	12841	6.0%
a	10919	5.1%
L	8048	3.8%
Other values (19)	64220	30.0%

what_data_visualization_libraries_or_tools_do_you_use_on_a_regular_basis
Text

Missing

Distinct	439
Distinct (%)	3.1%
Missing	5464
Missing (%)	27.7%
Memory size	154.2 KiB

Length

Max length	141
Median length	130
Mean length	30.0174
Min length	4

Characters and Unicode

Total characters	427838
Distinct characters	38
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	165 ?
Unique (%)	1.2%

Sample

1st row	Matplotlib
2nd row	Ggplot / ggplot2 , Matplotlib , Seaborn
3rd row	Matplotlib , Plotly / Plotly Express , Seaborn
4th row	Ggplot / ggplot2
5th row	Matplotlib , Plotly / Plotly Express , Bokeh , Seaborn

Value	Count	Frequency (%)
	24947	37.0%
matplotlib	10516	15.6%
seaborn	6905	10.3%
plotly	6434	9.6%
ggplot	4182	6.2%
ggplot2	4182	6.2%
express	3217	4.8%
shiny	1244	1.8%
none	1240	1.8%
d3.js	1078	1.6%
Other values (6)	3419	5.1%

Most occurring characters

Value	Count	Frequency (%)
	95205	22.3%
l	44819	10.5%
t	37656	8.8%
o	36340	8.5%
p	22741	5.3%
a	18138	4.2%
b	18065	4.2%
,	16998	4.0%
e	14614	3.4%
i	13121	3.1%
Other values (28)	110141	25.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	427838	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	95205	22.3%
l	44819	10.5%
t	37656	8.8%
o	36340	8.5%
p	22741	5.3%
a	18138	4.2%
b	18065	4.2%
,	16998	4.0%
e	14614	3.4%
i	13121	3.1%
Other values (28)	110141	25.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	427838	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	95205	22.3%
l	44819	10.5%
t	37656	8.8%
o	36340	8.5%
p	22741	5.3%
a	18138	4.2%
b	18065	4.2%
,	16998	4.0%
e	14614	3.4%
i	13121	3.1%
Other values (28)	110141	25.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	427838	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	95205	22.3%
l	44819	10.5%
t	37656	8.8%
o	36340	8.5%
p	22741	5.3%
a	18138	4.2%
b	18065	4.2%
,	16998	4.0%
e	14614	3.4%
i	13121	3.1%
Other values (28)	110141	25.7%

which_types_of_specialized_hardware_do_you_use_on_a_regular_basis
Categorical

Missing

Distinct	14
Distinct (%)	0.1%
Missing	5499
Missing (%)	27.9%
Memory size	154.2 KiB

CPUs, GPUs	5041
CPUs	5001
None / I do not know	2449
GPUs	1129
CPUs, GPUs, TPUs	348
Other values (9)	250

Length

Max length	23
Median length	20
Mean length	9.2723308
Min length	4

Characters and Unicode

Total characters	131834
Distinct characters	21
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	CPUs, GPUs
2nd row	CPUs, GPUs
3rd row	CPUs, GPUs
4th row	CPUs, GPUs
5th row	CPUs, GPUs

Common Values

Value	Count	Frequency (%)
CPUs, GPUs	5041	25.6%
CPUs	5001	25.4%
None / I do not know	2449	12.4%
GPUs	1129	5.7%
CPUs, GPUs, TPUs	348	1.8%
GPUs, TPUs	82	0.4%
Other	50	0.3%
TPUs	30	0.2%
CPUs, TPUs	30	0.2%
CPUs, GPUs, Other	27	0.1%
Other values (4)	31	0.2%
(Missing)	5499	27.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
cpus	10472	32.3%
gpus	6638	20.5%
none	2449	7.6%
	2449	7.6%
i	2449	7.6%
do	2449	7.6%
not	2449	7.6%
know	2449	7.6%
tpus	496	1.5%
other	108	0.3%

Most occurring characters

Value	Count	Frequency (%)
	18190	13.8%
P	17606	13.4%
U	17606	13.4%
s	17606	13.4%
C	10472	7.9%
o	9796	7.4%
n	7347	5.6%
G	6638	5.0%
,	5945	4.5%
t	2557	1.9%
Other values (11)	18071	13.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	131834	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	18190	13.8%
P	17606	13.4%
U	17606	13.4%
s	17606	13.4%
C	10472	7.9%
o	9796	7.4%
n	7347	5.6%
G	6638	5.0%
,	5945	4.5%
t	2557	1.9%
Other values (11)	18071	13.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	131834	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	18190	13.8%
P	17606	13.4%
U	17606	13.4%
s	17606	13.4%
C	10472	7.9%
o	9796	7.4%
n	7347	5.6%
G	6638	5.0%
,	5945	4.5%
t	2557	1.9%
Other values (11)	18071	13.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	131834	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	18190	13.8%
P	17606	13.4%
U	17606	13.4%
s	17606	13.4%
C	10472	7.9%
o	9796	7.4%
n	7347	5.6%
G	6638	5.0%
,	5945	4.5%
t	2557	1.9%
Other values (11)	18071	13.7%

which_of_the_following_ml_algorithms_do_you_use_on_a_regular_basis
Text

Missing

Distinct	684
Distinct (%)	4.9%
Missing	5629
Missing (%)	28.5%
Memory size	154.2 KiB

Length

Max length	336
Median length	288
Mean length	101.29813
Min length	4

Characters and Unicode

Total characters	1427088
Distinct characters	43
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	232 ?
Unique (%)	1.6%

Sample

1st row	Linear or Logistic Regression
2nd row	Linear or Logistic Regression, Convolutional Neural Networks
3rd row	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc)
4th row	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc), Bayesian Approaches, Convolutional Neural Networks, Generative Adversarial Networks, Recurrent Neural Networks
5th row	Linear or Logistic Regression, Dense Neural Networks (MLPs, etc), Convolutional Neural Networks, Recurrent Neural Networks

Value	Count	Frequency (%)
or	18713	10.5%
networks	14046	7.9%
neural	12162	6.8%
linear	10223	5.7%
logistic	10223	5.7%
regression	10223	5.7%
etc	9769	5.5%
decision	8490	4.8%
trees	8490	4.8%
random	8490	4.8%
Other values (20)	67134	37.7%

Most occurring characters

Value	Count	Frequency (%)
	163875	11.5%
e	139846	9.8%
o	125597	8.8%
s	112138	7.9%
r	106194	7.4%
i	92023	6.4%
n	79316	5.6%
t	76694	5.4%
a	64148	4.5%
,	46623	3.3%
Other values (33)	420634	29.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1427088	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	163875	11.5%
e	139846	9.8%
o	125597	8.8%
s	112138	7.9%
r	106194	7.4%
i	92023	6.4%
n	79316	5.6%
t	76694	5.4%
a	64148	4.5%
,	46623	3.3%
Other values (33)	420634	29.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1427088	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	163875	11.5%
e	139846	9.8%
o	125597	8.8%
s	112138	7.9%
r	106194	7.4%
i	92023	6.4%
n	79316	5.6%
t	76694	5.4%
a	64148	4.5%
,	46623	3.3%
Other values (33)	420634	29.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1427088	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	163875	11.5%
e	139846	9.8%
o	125597	8.8%
s	112138	7.9%
r	106194	7.4%
i	92023	6.4%
n	79316	5.6%
t	76694	5.4%
a	64148	4.5%
,	46623	3.3%
Other values (33)	420634	29.5%

which_categories_of_ml_tools_do_you_use_on_a_regular_basis
Text

Missing

Distinct	92
Distinct (%)	0.7%
Missing	5802
Missing (%)	29.4%
Memory size	154.2 KiB

Length

Max length	374
Median length	4
Mean length	44.514625
Min length	4

Characters and Unicode

Total characters	619421
Distinct characters	41
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	15 ?
Unique (%)	0.1%

Sample

1st row	None
2nd row	Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)
3rd row	None
4th row	Automated model selection (e.g. auto-sklearn, xcessiv), Automated hyperparameter tuning (e.g. hyperopt, ray.tune), Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)
5th row	Automated data augmentation (e.g. imgaug, albumentations), Automated feature engineering/selection (e.g. tpot, boruta_py), Automated model selection (e.g. auto-sklearn, xcessiv), Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)

Value	Count	Frequency (%)
e.g	9911	13.4%
automated	8733	11.8%
none	7822	10.6%
model	3650	4.9%
selection	3200	4.3%
auto-sklearn	3200	4.3%
xcessiv	3200	4.3%
data	1800	2.4%
augmentation	1800	2.4%
imgaug	1800	2.4%
Other values (24)	28741	38.9%

Most occurring characters

Value	Count	Frequency (%)
e	74310	12.0%
	59942	9.7%
t	52616	8.5%
o	43566	7.0%
a	39055	6.3%
n	35582	5.7%
u	27883	4.5%
i	23255	3.8%
.	21600	3.5%
s	21439	3.5%
Other values (31)	220173	35.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	619421	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	74310	12.0%
	59942	9.7%
t	52616	8.5%
o	43566	7.0%
a	39055	6.3%
n	35582	5.7%
u	27883	4.5%
i	23255	3.8%
.	21600	3.5%
s	21439	3.5%
Other values (31)	220173	35.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	619421	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	74310	12.0%
	59942	9.7%
t	52616	8.5%
o	43566	7.0%
a	39055	6.3%
n	35582	5.7%
u	27883	4.5%
i	23255	3.8%
.	21600	3.5%
s	21439	3.5%
Other values (31)	220173	35.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	619421	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	74310	12.0%
	59942	9.7%
t	52616	8.5%
o	43566	7.0%
a	39055	6.3%
n	35582	5.7%
u	27883	4.5%
i	23255	3.8%
.	21600	3.5%
s	21439	3.5%
Other values (31)	220173	35.5%

which_categories_of_computer_vision_methods_do_you_use_on_a_regular_basis
Categorical

Missing

Distinct	49
Distinct (%)	0.9%
Missing	14225
Missing (%)	72.1%
Memory size	154.2 KiB

None	1203
Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	560
General purpose image/video tools (PIL, cv2, skimage, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	366
General purpose image/video tools (PIL, cv2, skimage, etc), Image segmentation methods (U-Net, Mask R-CNN, etc), Object detection methods (YOLOv3, RetinaNet, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc), Generative Networks (GAN, VAE, etc)	341
General purpose image/video tools (PIL, cv2, skimage, etc), Image segmentation methods (U-Net, Mask R-CNN, etc), Object detection methods (YOLOv3, RetinaNet, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	326
Other values (44)	2696

Length

Max length	324
Median length	271
Mean length	136.52203
Min length	4

Characters and Unicode

Total characters	749779
Distinct characters	46
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	9 ?
Unique (%)	0.2%

Sample

1st row	General purpose image/video tools (PIL, cv2, skimage, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)
2nd row	None
3rd row	General purpose image/video tools (PIL, cv2, skimage, etc), Image segmentation methods (U-Net, Mask R-CNN, etc), Object detection methods (YOLOv3, RetinaNet, etc)
4th row	General purpose image/video tools (PIL, cv2, skimage, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)
5th row	General purpose image/video tools (PIL, cv2, skimage, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)

Common Values

Value	Count	Frequency (%)
None	1203	6.1%
Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	560	2.8%
General purpose image/video tools (PIL, cv2, skimage, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	366	1.9%
General purpose image/video tools (PIL, cv2, skimage, etc), Image segmentation methods (U-Net, Mask R-CNN, etc), Object detection methods (YOLOv3, RetinaNet, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc), Generative Networks (GAN, VAE, etc)	341	1.7%
General purpose image/video tools (PIL, cv2, skimage, etc), Image segmentation methods (U-Net, Mask R-CNN, etc), Object detection methods (YOLOv3, RetinaNet, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	326	1.7%
General purpose image/video tools (PIL, cv2, skimage, etc), Image segmentation methods (U-Net, Mask R-CNN, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	243	1.2%
Image segmentation methods (U-Net, Mask R-CNN, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	237	1.2%
General purpose image/video tools (PIL, cv2, skimage, etc)	233	1.2%
Image segmentation methods (U-Net, Mask R-CNN, etc), Object detection methods (YOLOv3, RetinaNet, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	229	1.2%
General purpose image/video tools (PIL, cv2, skimage, etc), Object detection methods (YOLOv3, RetinaNet, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	224	1.1%
Other values (39)	1530	7.8%
(Missing)	14225	72.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
etc	10408	11.0%
general	5394	5.7%
purpose	5394	5.7%
image	5248	5.5%
networks	4268	4.5%
methods	3933	4.2%
other	3238	3.4%
and	3187	3.4%
classification	3187	3.4%
inception	3187	3.4%
Other values (22)	47148	49.8%

Most occurring characters

Value	Count	Frequency (%)
e	95383	12.7%
	89100	11.9%
t	62987	8.4%
,	41941	5.6%
o	34913	4.7%
s	34879	4.7%
n	34666	4.6%
i	32629	4.4%
a	31692	4.2%
c	29107	3.9%
Other values (36)	262482	35.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	749779	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	95383	12.7%
	89100	11.9%
t	62987	8.4%
,	41941	5.6%
o	34913	4.7%
s	34879	4.7%
n	34666	4.6%
i	32629	4.4%
a	31692	4.2%
c	29107	3.9%
Other values (36)	262482	35.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	749779	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	95383	12.7%
	89100	11.9%
t	62987	8.4%
,	41941	5.6%
o	34913	4.7%
s	34879	4.7%
n	34666	4.6%
i	32629	4.4%
a	31692	4.2%
c	29107	3.9%
Other values (36)	262482	35.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	749779	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	95383	12.7%
	89100	11.9%
t	62987	8.4%
,	41941	5.6%
o	34913	4.7%
s	34879	4.7%
n	34666	4.6%
i	32629	4.4%
a	31692	4.2%
c	29107	3.9%
Other values (36)	262482	35.0%

which_of_the_following_natural_language_processing_nlp_methods_do_you_use_on_a_regular_basis
Categorical

Missing

Distinct	28
Distinct (%)	0.8%
Missing	16135
Missing (%)	81.8%
Memory size	154.2 KiB

None	1027
Word embeddings/vectors (GLoVe, fastText, word2vec)	616
Word embeddings/vectors (GLoVe, fastText, word2vec), Encoder-decorder models (seq2seq, vanilla transformers)	498
Word embeddings/vectors (GLoVe, fastText, word2vec), Encoder-decorder models (seq2seq, vanilla transformers), Contextualized embeddings (ELMo, CoVe), Transformer language models (GPT-2, BERT, XLnet, etc)	268
Word embeddings/vectors (GLoVe, fastText, word2vec), Encoder-decorder models (seq2seq, vanilla transformers), Transformer language models (GPT-2, BERT, XLnet, etc)	250
Other values (23)	923

Length

Max length	210
Median length	170
Mean length	74.985204
Min length	4

Characters and Unicode

Total characters	268597
Distinct characters	43
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5 ?
Unique (%)	0.1%

Sample

1st row	Word embeddings/vectors (GLoVe, fastText, word2vec), Encoder-decorder models (seq2seq, vanilla transformers)
2nd row	Word embeddings/vectors (GLoVe, fastText, word2vec), Encoder-decorder models (seq2seq, vanilla transformers)
3rd row	Word embeddings/vectors (GLoVe, fastText, word2vec)
4th row	Word embeddings/vectors (GLoVe, fastText, word2vec), Encoder-decorder models (seq2seq, vanilla transformers), Contextualized embeddings (ELMo, CoVe), Transformer language models (GPT-2, BERT, XLnet, etc)
5th row	None

Common Values

Value	Count	Frequency (%)
None	1027	5.2%
Word embeddings/vectors (GLoVe, fastText, word2vec)	616	3.1%
Word embeddings/vectors (GLoVe, fastText, word2vec), Encoder-decorder models (seq2seq, vanilla transformers)	498	2.5%
Word embeddings/vectors (GLoVe, fastText, word2vec), Encoder-decorder models (seq2seq, vanilla transformers), Contextualized embeddings (ELMo, CoVe), Transformer language models (GPT-2, BERT, XLnet, etc)	268	1.4%
Word embeddings/vectors (GLoVe, fastText, word2vec), Encoder-decorder models (seq2seq, vanilla transformers), Transformer language models (GPT-2, BERT, XLnet, etc)	250	1.3%
Word embeddings/vectors (GLoVe, fastText, word2vec), Transformer language models (GPT-2, BERT, XLnet, etc)	230	1.2%
Encoder-decorder models (seq2seq, vanilla transformers)	188	1.0%
Transformer language models (GPT-2, BERT, XLnet, etc)	115	0.6%
Word embeddings/vectors (GLoVe, fastText, word2vec), Contextualized embeddings (ELMo, CoVe), Transformer language models (GPT-2, BERT, XLnet, etc)	79	0.4%
Word embeddings/vectors (GLoVe, fastText, word2vec), Encoder-decorder models (seq2seq, vanilla transformers), Contextualized embeddings (ELMo, CoVe)	76	0.4%
Other values (18)	235	1.2%
(Missing)	16135	81.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
models	2399	8.6%
embeddings/vectors	2115	7.6%
glove	2115	7.6%
fasttext	2115	7.6%
word2vec	2115	7.6%
word	2115	7.6%
seq2seq	1368	4.9%
encoder-decorder	1368	4.9%
vanilla	1368	4.9%
transformers	1368	4.9%
Other values (12)	9510	34.0%

Most occurring characters

Value	Count	Frequency (%)
e	31307	11.7%
	24374	9.1%
o	18707	7.0%
r	17695	6.6%
d	16649	6.2%
s	15809	5.9%
,	11823	4.4%
n	11463	4.3%
t	10948	4.1%
a	9874	3.7%
Other values (33)	99948	37.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	268597	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	31307	11.7%
	24374	9.1%
o	18707	7.0%
r	17695	6.6%
d	16649	6.2%
s	15809	5.9%
,	11823	4.4%
n	11463	4.3%
t	10948	4.1%
a	9874	3.7%
Other values (33)	99948	37.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	268597	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	31307	11.7%
	24374	9.1%
o	18707	7.0%
r	17695	6.6%
d	16649	6.2%
s	15809	5.9%
,	11823	4.4%
n	11463	4.3%
t	10948	4.1%
a	9874	3.7%
Other values (33)	99948	37.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	268597	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	31307	11.7%
	24374	9.1%
o	18707	7.0%
r	17695	6.6%
d	16649	6.2%
s	15809	5.9%
,	11823	4.4%
n	11463	4.3%
t	10948	4.1%
a	9874	3.7%
Other values (33)	99948	37.2%

which_of_the_following_machine_learning_frameworks_do_you_use_on_a_regular_basis
Text

Missing

Distinct	584
Distinct (%)	4.2%
Missing	5964
Missing (%)	30.2%
Memory size	154.2 KiB

Length

Max length	129
Median length	108
Mean length	36.025522
Min length	4

Characters and Unicode

Total characters	495459
Distinct characters	37
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	202 ?
Unique (%)	1.5%

Sample

1st row	None
2nd row	Scikit-learn , TensorFlow , Keras , RandomForest
3rd row	Scikit-learn , RandomForest, Xgboost , LightGBM
4th row	Scikit-learn , TensorFlow , Keras , RandomForest, Xgboost , Caret
5th row	Scikit-learn , TensorFlow , Keras , PyTorch

Value	Count	Frequency (%)
	23108	35.9%
scikit-learn	9390	14.6%
tensorflow	5822	9.0%
keras	5756	8.9%
randomforest	4524	7.0%
xgboost	4243	6.6%
pytorch	3412	5.3%
lightgbm	2166	3.4%
none	1720	2.7%
caret	1139	1.8%
Other values (4)	3111	4.8%

Most occurring characters

Value	Count	Frequency (%)
	114840	23.2%
o	34310	6.9%
r	31295	6.3%
e	28693	5.8%
,	26620	5.4%
a	23617	4.8%
i	22805	4.6%
t	22753	4.6%
n	21456	4.3%
s	21294	4.3%
Other values (27)	147776	29.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	495459	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	114840	23.2%
o	34310	6.9%
r	31295	6.3%
e	28693	5.8%
,	26620	5.4%
a	23617	4.8%
i	22805	4.6%
t	22753	4.6%
n	21456	4.3%
s	21294	4.3%
Other values (27)	147776	29.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	495459	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	114840	23.2%
o	34310	6.9%
r	31295	6.3%
e	28693	5.8%
,	26620	5.4%
a	23617	4.8%
i	22805	4.6%
t	22753	4.6%
n	21456	4.3%
s	21294	4.3%
Other values (27)	147776	29.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	495459	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	114840	23.2%
o	34310	6.9%
r	31295	6.3%
e	28693	5.8%
,	26620	5.4%
a	23617	4.8%
i	22805	4.6%
t	22753	4.6%
n	21456	4.3%
s	21294	4.3%
Other values (27)	147776	29.8%

which_of_the_following_cloud_computing_platforms_do_you_use_on_a_regular_basis
Text

Missing

Distinct	183
Distinct (%)	2.6%
Missing	12592
Missing (%)	63.9%
Memory size	154.2 KiB

Length

Max length	189
Median length	170
Mean length	26.534316
Min length	4

Characters and Unicode

Total characters	189057
Distinct characters	38
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	82 ?
Unique (%)	1.2%

Sample

1st row	Microsoft Azure
2nd row	Amazon Web Services (AWS)
3rd row	Google Cloud Platform (GCP) , Amazon Web Services (AWS) , Microsoft Azure
4th row	None
5th row	Google Cloud Platform (GCP)

Value	Count	Frequency (%)
cloud	3233	10.9%
web	2758	9.3%
services	2758	9.3%
aws	2758	9.3%
amazon	2758	9.3%
	2621	8.9%
none	2229	7.5%
google	2134	7.2%
platform	2134	7.2%
gcp	2134	7.2%
Other values (11)	4059	13.7%

Most occurring characters

Value	Count	Frequency (%)
	34524	18.3%
o	17449	9.2%
e	14804	7.8%
r	8222	4.3%
l	7888	4.2%
A	7072	3.7%
S	5721	3.0%
a	5638	3.0%
W	5516	2.9%
C	5367	2.8%
Other values (28)	76856	40.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	189057	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	34524	18.3%
o	17449	9.2%
e	14804	7.8%
r	8222	4.3%
l	7888	4.2%
A	7072	3.7%
S	5721	3.0%
a	5638	3.0%
W	5516	2.9%
C	5367	2.8%
Other values (28)	76856	40.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	189057	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	34524	18.3%
o	17449	9.2%
e	14804	7.8%
r	8222	4.3%
l	7888	4.2%
A	7072	3.7%
S	5721	3.0%
a	5638	3.0%
W	5516	2.9%
C	5367	2.8%
Other values (28)	76856	40.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	189057	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	34524	18.3%
o	17449	9.2%
e	14804	7.8%
r	8222	4.3%
l	7888	4.2%
A	7072	3.7%
S	5721	3.0%
a	5638	3.0%
W	5516	2.9%
C	5367	2.8%
Other values (28)	76856	40.7%

which_specific_cloud_computing_products_do_you_use_on_a_regular_basis
Text

Missing

Distinct	336
Distinct (%)	4.7%
Missing	12617
Missing (%)	64.0%
Memory size	154.2 KiB

Length

Max length	231
Median length	224
Mean length	27.010986
Min length	4

Characters and Unicode

Total characters	191778
Distinct characters	39
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	155 ?
Unique (%)	2.2%

Sample

1st row	Azure Virtual Machines, Azure Container Service
2nd row	AWS Elastic Compute Cloud (EC2)
3rd row	Google Compute Engine (GCE), AWS Lambda, Azure Virtual Machines
4th row	None
5th row	AWS Elastic Compute Cloud (EC2)

Value	Count	Frequency (%)
aws	3281	11.2%
none	3155	10.7%
google	2963	10.1%
compute	2948	10.0%
cloud	2512	8.5%
engine	2261	7.7%
elastic	2121	7.2%
ec2	1810	6.2%
azure	1233	4.2%
gce	1138	3.9%
Other values (11)	6001	20.4%

Most occurring characters

Value	Count	Frequency (%)
	22323	11.6%
e	16711	8.7%
o	15638	8.2%
n	11546	6.0%
C	8803	4.6%
u	8759	4.6%
l	8745	4.6%
t	8364	4.4%
i	7550	3.9%
E	7330	3.8%
Other values (29)	76009	39.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	191778	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	22323	11.6%
e	16711	8.7%
o	15638	8.2%
n	11546	6.0%
C	8803	4.6%
u	8759	4.6%
l	8745	4.6%
t	8364	4.4%
i	7550	3.9%
E	7330	3.8%
Other values (29)	76009	39.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	191778	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	22323	11.6%
e	16711	8.7%
o	15638	8.2%
n	11546	6.0%
C	8803	4.6%
u	8759	4.6%
l	8745	4.6%
t	8364	4.4%
i	7550	3.9%
E	7330	3.8%
Other values (29)	76009	39.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	191778	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	22323	11.6%
e	16711	8.7%
o	15638	8.2%
n	11546	6.0%
C	8803	4.6%
u	8759	4.6%
l	8745	4.6%
t	8364	4.4%
i	7550	3.9%
E	7330	3.8%
Other values (29)	76009	39.6%

which_specific_big_data_analytics_products_do_you_use_on_a_regular_basis
Text

Missing

Distinct	287
Distinct (%)	4.1%
Missing	12639
Missing (%)	64.1%
Memory size	154.2 KiB

Length

Max length	173
Median length	4
Mean length	13.882311
Min length	4

Characters and Unicode

Total characters	98259
Distinct characters	40
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	143 ?
Unique (%)	2.0%

Sample

1st row	Databricks, Microsoft Analysis Services
2nd row	AWS Elastic MapReduce
3rd row	Google BigQuery, Databricks
4th row	None
5th row	Google Cloud Dataflow

Value	Count	Frequency (%)
none	4133	27.6%
google	1881	12.5%
aws	1641	10.9%
bigquery	958	6.4%
cloud	923	6.2%
databricks	604	4.0%
redshift	562	3.7%
dataflow	525	3.5%
elastic	429	2.9%
mapreduce	429	2.9%
Other values (8)	2914	19.4%

Most occurring characters

Value	Count	Frequency (%)
e	10482	10.7%
o	10195	10.4%
	7921	8.1%
n	5209	5.3%
a	4877	5.0%
i	4393	4.5%
l	4184	4.3%
N	4133	4.2%
s	3861	3.9%
t	3503	3.6%
Other values (30)	39501	40.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	98259	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	10482	10.7%
o	10195	10.4%
	7921	8.1%
n	5209	5.3%
a	4877	5.0%
i	4393	4.5%
l	4184	4.3%
N	4133	4.2%
s	3861	3.9%
t	3503	3.6%
Other values (30)	39501	40.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	98259	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	10482	10.7%
o	10195	10.4%
	7921	8.1%
n	5209	5.3%
a	4877	5.0%
i	4393	4.5%
l	4184	4.3%
N	4133	4.2%
s	3861	3.9%
t	3503	3.6%
Other values (30)	39501	40.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	98259	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	10482	10.7%
o	10195	10.4%
	7921	8.1%
n	5209	5.3%
a	4877	5.0%
i	4393	4.5%
l	4184	4.3%
N	4133	4.2%
s	3861	3.9%
t	3503	3.6%
Other values (30)	39501	40.2%

which_of_the_following_machine_learning_products_do_you_use_on_a_regular_basis
Text

Missing

Distinct	272
Distinct (%)	3.9%
Missing	12667
Missing (%)	64.2%
Memory size	154.2 KiB

Length

Max length	219
Median length	4
Mean length	16.253617
Min length	3

Characters and Unicode

Total characters	114588
Distinct characters	34
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	126 ?
Unique (%)	1.8%

Sample

1st row	Azure Machine Learning Studio
2nd row	RapidMiner
3rd row	SAS, Azure Machine Learning Studio, Google Cloud Machine Learning Engine
4th row	None
5th row	Google Cloud Translation

Value	Count	Frequency (%)
none	4313	25.3%
cloud	2111	12.4%
google	2111	12.4%
machine	1167	6.8%
learning	1167	6.8%
engine	586	3.4%
azure	581	3.4%
studio	581	3.4%
amazon	569	3.3%
sagemaker	569	3.3%
Other values (9)	3283	19.3%

Most occurring characters

Value	Count	Frequency (%)
e	13685	11.9%
o	13339	11.6%
n	11222	9.8%
	9988	8.7%
a	6954	6.1%
l	5355	4.7%
g	5233	4.6%
i	5090	4.4%
N	4713	4.1%
u	4491	3.9%
Other values (24)	34518	30.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	114588	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	13685	11.9%
o	13339	11.6%
n	11222	9.8%
	9988	8.7%
a	6954	6.1%
l	5355	4.7%
g	5233	4.6%
i	5090	4.4%
N	4713	4.1%
u	4491	3.9%
Other values (24)	34518	30.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	114588	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	13685	11.9%
o	13339	11.6%
n	11222	9.8%
	9988	8.7%
a	6954	6.1%
l	5355	4.7%
g	5233	4.6%
i	5090	4.4%
N	4713	4.1%
u	4491	3.9%
Other values (24)	34518	30.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	114588	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	13685	11.9%
o	13339	11.6%
n	11222	9.8%
	9988	8.7%
a	6954	6.1%
l	5355	4.7%
g	5233	4.6%
i	5090	4.4%
N	4713	4.1%
u	4491	3.9%
Other values (24)	34518	30.1%

which_automated_machine_learning_tools_or_partial_auto_ml_tools_do_you_use_on_a_regular_basis
Text

Missing

Distinct	201
Distinct (%)	2.9%
Missing	12702
Missing (%)	64.4%
Memory size	154.2 KiB

Length

Max length	153
Median length	4
Mean length	9.4597292
Min length	4

Characters and Unicode

Total characters	66360
Distinct characters	39
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	100 ?
Unique (%)	1.4%

Sample

1st row	None
2nd row	Auto-Keras
3rd row	Google AutoML , Tpot , Auto-Keras , Auto-Sklearn , Auto_ml
4th row	None
5th row	Google AutoML

Value	Count	Frequency (%)
none	5175	47.2%
	1266	11.6%
automl	860	7.8%
auto-sklearn	756	6.9%
google	498	4.5%
auto-keras	465	4.2%
auto_ml	279	2.5%
h20	277	2.5%
driverless	277	2.5%
ai	277	2.5%
Other values (6)	831	7.6%

Most occurring characters

Value	Count	Frequency (%)
	10742	16.2%
o	9182	13.8%
e	7608	11.5%
n	5931	8.9%
N	5175	7.8%
t	3201	4.8%
A	2637	4.0%
u	2360	3.6%
r	2098	3.2%
a	1945	2.9%
Other values (29)	15481	23.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	66360	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	10742	16.2%
o	9182	13.8%
e	7608	11.5%
n	5931	8.9%
N	5175	7.8%
t	3201	4.8%
A	2637	4.0%
u	2360	3.6%
r	2098	3.2%
a	1945	2.9%
Other values (29)	15481	23.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	66360	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	10742	16.2%
o	9182	13.8%
e	7608	11.5%
n	5931	8.9%
N	5175	7.8%
t	3201	4.8%
A	2637	4.0%
u	2360	3.6%
r	2098	3.2%
a	1945	2.9%
Other values (29)	15481	23.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	66360	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	10742	16.2%
o	9182	13.8%
e	7608	11.5%
n	5931	8.9%
N	5175	7.8%
t	3201	4.8%
A	2637	4.0%
u	2360	3.6%
r	2098	3.2%
a	1945	2.9%
Other values (29)	15481	23.3%

which_of_the_following_relational_database_products_do_you_use_on_a_regular_basis
Text

Missing

Distinct	454
Distinct (%)	6.5%
Missing	12723
Missing (%)	64.5%
Memory size	154.2 KiB

Length

Max length	168
Median length	146
Mean length	24.700743
Min length	4

Characters and Unicode

Total characters	172757
Distinct characters	36
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	196 ?
Unique (%)	2.8%

Sample

1st row	Azure SQL Database
2nd row	PostgresSQL, AWS Relational Database Service
3rd row	MySQL, PostgresSQL
4th row	MySQL
5th row	MySQL

Value	Count	Frequency (%)
mysql	3122	13.2%
sql	2857	12.1%
microsoft	2399	10.2%
database	2259	9.6%
postgressql	2160	9.2%
server	1852	7.9%
sqlite	1527	6.5%
none	1245	5.3%
oracle	1192	5.1%
aws	1003	4.3%
Other values (8)	3956	16.8%

Most occurring characters

Value	Count	Frequency (%)
	16578	9.6%
e	15690	9.1%
S	13109	7.6%
r	10809	6.3%
o	10784	6.2%
s	10072	5.8%
Q	9666	5.6%
L	9666	5.6%
a	9560	5.5%
t	9220	5.3%
Other values (26)	57603	33.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	172757	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	16578	9.6%
e	15690	9.1%
S	13109	7.6%
r	10809	6.3%
o	10784	6.2%
s	10072	5.8%
Q	9666	5.6%
L	9666	5.6%
a	9560	5.5%
t	9220	5.3%
Other values (26)	57603	33.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	172757	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	16578	9.6%
e	15690	9.1%
S	13109	7.6%
r	10809	6.3%
o	10784	6.2%
s	10072	5.8%
Q	9666	5.6%
L	9666	5.6%
a	9560	5.5%
t	9220	5.3%
Other values (26)	57603	33.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	172757	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	16578	9.6%
e	15690	9.1%
S	13109	7.6%
r	10809	6.3%
o	10784	6.2%
s	10072	5.8%
Q	9666	5.6%
L	9666	5.6%
a	9560	5.5%
t	9220	5.3%
Other values (26)	57603	33.3%

Correlations

	approximately_how_many_individuals_are_responsible_for_data_science_workloads_at_your_place_of_business	approximately_how_much_money_have_you_spent_on_machine_learning_and_or_cloud_computing_products_at_your_work_in_the_past_5_years	does_your_current_employer_incorporate_machine_learning_methods_into_their_business	for_how_many_years_have_you_used_machine_learning_methods	have_you_ever_used_a_tpu_tensor_processing_unit	how_long_have_you_been_writing_code_to_analyze_data_at_work_or_at_school	select_the_title_most_similar_to_your_current_role_or_most_recent_title_if_retired	what_is_the_highest_level_of_formal_education_that_you_have_attained_or_plan_to_attain_within_the_next_2_years	what_is_the_size_of_the_company_where_you_are_employed	what_is_your_age_#_years	what_is_your_current_yearly_compensation_approximate_$_usd	what_is_your_gender	what_programming_language_would_you_recommend_an_aspiring_data_scientist_to_learn_first	which_categories_of_computer_vision_methods_do_you_use_on_a_regular_basis	which_of_the_following_natural_language_processing_nlp_methods_do_you_use_on_a_regular_basis	which_types_of_specialized_hardware_do_you_use_on_a_regular_basis
approximately_how_many_individuals_are_responsible_for_data_science_workloads_at_your_place_of_business	1.000	0.155	0.244	0.112	0.034	0.118	0.107	0.057	0.302	0.038	0.108	0.014	0.025	0.000	0.055	0.036
approximately_how_much_money_have_you_spent_on_machine_learning_and_or_cloud_computing_products_at_your_work_in_the_past_5_years	0.155	1.000	0.173	0.158	0.073	0.153	0.091	0.046	0.103	0.096	0.201	0.036	0.037	0.052	0.051	0.089
does_your_current_employer_incorporate_machine_learning_methods_into_their_business	0.244	0.173	1.000	0.201	0.052	0.162	0.161	0.066	0.120	0.059	0.141	0.030	0.041	0.059	0.090	0.099
for_how_many_years_have_you_used_machine_learning_methods	0.112	0.158	0.201	1.000	0.089	0.475	0.180	0.161	0.035	0.185	0.165	0.046	0.048	0.089	0.096	0.109
have_you_ever_used_a_tpu_tensor_processing_unit	0.034	0.073	0.052	0.089	1.000	0.053	0.039	0.021	0.024	0.034	0.055	0.041	0.036	0.114	0.109	0.278
how_long_have_you_been_writing_code_to_analyze_data_at_work_or_at_school	0.118	0.153	0.162	0.475	0.053	1.000	0.188	0.152	0.058	0.271	0.205	0.048	0.066	0.069	0.078	0.077
select_the_title_most_similar_to_your_current_role_or_most_recent_title_if_retired	0.107	0.091	0.161	0.180	0.039	0.188	1.000	0.182	0.050	0.194	0.069	0.056	0.075	0.044	0.062	0.070
what_is_the_highest_level_of_formal_education_that_you_have_attained_or_plan_to_attain_within_the_next_2_years	0.057	0.046	0.066	0.161	0.021	0.152	0.182	1.000	0.067	0.186	0.086	0.080	0.051	0.065	0.000	0.036
what_is_the_size_of_the_company_where_you_are_employed	0.302	0.103	0.120	0.035	0.024	0.058	0.050	0.067	1.000	0.083	0.139	0.013	0.039	0.018	0.043	0.042
what_is_your_age_#_years	0.038	0.096	0.059	0.185	0.034	0.271	0.194	0.186	0.083	1.000	0.149	0.062	0.061	0.040	0.030	0.029
what_is_your_current_yearly_compensation_approximate_$_usd	0.108	0.201	0.141	0.165	0.055	0.205	0.069	0.086	0.139	0.149	1.000	0.059	0.040	0.055	0.050	0.045
what_is_your_gender	0.014	0.036	0.030	0.046	0.041	0.048	0.056	0.080	0.013	0.062	0.059	1.000	0.053	0.088	0.041	0.110
what_programming_language_would_you_recommend_an_aspiring_data_scientist_to_learn_first	0.025	0.037	0.041	0.048	0.036	0.066	0.075	0.051	0.039	0.061	0.040	0.053	1.000	0.060	0.078	0.063
which_categories_of_computer_vision_methods_do_you_use_on_a_regular_basis	0.000	0.052	0.059	0.089	0.114	0.069	0.044	0.065	0.018	0.040	0.055	0.088	0.060	1.000	0.139	0.174
which_of_the_following_natural_language_processing_nlp_methods_do_you_use_on_a_regular_basis	0.055	0.051	0.090	0.096	0.109	0.078	0.062	0.000	0.043	0.030	0.050	0.041	0.078	0.139	1.000	0.099
which_types_of_specialized_hardware_do_you_use_on_a_regular_basis	0.036	0.089	0.099	0.109	0.278	0.077	0.070	0.036	0.042	0.029	0.045	0.110	0.063	0.174	0.099	1.000

Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

	what_is_your_age_#_years	what_is_your_gender	in_which_country_do_you_currently_reside	what_is_the_highest_level_of_formal_education_that_you_have_attained_or_plan_to_attain_within_the_next_2_years	select_the_title_most_similar_to_your_current_role_or_most_recent_title_if_retired	what_is_the_size_of_the_company_where_you_are_employed	approximately_how_many_individuals_are_responsible_for_data_science_workloads_at_your_place_of_business	does_your_current_employer_incorporate_machine_learning_methods_into_their_business	what_is_your_current_yearly_compensation_approximate_$_usd	approximately_how_much_money_have_you_spent_on_machine_learning_and_or_cloud_computing_products_at_your_work_in_the_past_5_years	what_is_the_primary_tool_that_you_use_at_work_or_school_to_analyze_data	how_long_have_you_been_writing_code_to_analyze_data_at_work_or_at_school	what_programming_language_would_you_recommend_an_aspiring_data_scientist_to_learn_first	have_you_ever_used_a_tpu_tensor_processing_unit	for_how_many_years_have_you_used_machine_learning_methods	select_any_activities_that_make_up_an_important_part_of_your_role_at_work	who_what_are_your_favorite_media_sources_that_report_on_data_science_topics	on_which_platforms_have_you_begun_or_completed_data_science_courses	which_of_the_following_integrated_development_environments_id_es_do_you_use_on_a_regular_basis	which_of_the_following_hosted_notebook_products_do_you_use_on_a_regular_basis	what_programming_languages_do_you_use_on_a_regular_basis	what_data_visualization_libraries_or_tools_do_you_use_on_a_regular_basis	which_types_of_specialized_hardware_do_you_use_on_a_regular_basis	which_of_the_following_ml_algorithms_do_you_use_on_a_regular_basis	which_categories_of_ml_tools_do_you_use_on_a_regular_basis	which_categories_of_computer_vision_methods_do_you_use_on_a_regular_basis	which_of_the_following_natural_language_processing_nlp_methods_do_you_use_on_a_regular_basis	which_of_the_following_machine_learning_frameworks_do_you_use_on_a_regular_basis	which_of_the_following_cloud_computing_platforms_do_you_use_on_a_regular_basis	which_specific_cloud_computing_products_do_you_use_on_a_regular_basis	which_specific_big_data_analytics_products_do_you_use_on_a_regular_basis	which_of_the_following_machine_learning_products_do_you_use_on_a_regular_basis	which_automated_machine_learning_tools_or_partial_auto_ml_tools_do_you_use_on_a_regular_basis	which_of_the_following_relational_database_products_do_you_use_on_a_regular_basis
0	22-24	Male	France	Master’s degree	Software Engineer	1000-9,999 employees	0	I do not know	30,000-39,999	$0 (USD)	Basic statistical software (Microsoft Excel, Google Sheets, etc.), 0, -1, -1, -1, -1	1-2 years	Python	Never	1-2 years	NaN	Twitter (data science influencers), Kaggle (forums, blog, social media, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)	Coursera, DataCamp, Kaggle Courses (i.e. Kaggle Learn), Udemy	Jupyter (JupyterLab, Jupyter Notebooks, etc) , RStudio , PyCharm , MATLAB , Spyder	None	Python, R, SQL, Java, Javascript, MATLAB	Matplotlib	CPUs, GPUs	Linear or Logistic Regression	None	NaN	NaN	None	NaN	NaN	NaN	NaN	NaN	NaN
1	40-44	Male	India	Professional degree	Software Engineer	> 10,000 employees	20+	We have well established ML methods (i.e., models in production for more than 2 years)	5,000-7,499	> $100,000 ($USD)	Cloud-based data software & APIs (AWS, GCP, Azure, etc.), -1, -1, -1, -1, 0	I have never written code	NaN	NaN	NaN	Analyze and understand data to influence product or business decisions, Build and/or run the data infrastructure that my business uses for storing, analyzing, and operationalizing data, Build prototypes to explore applying machine learning to new areas, Build and/or run a machine learning service that operationally improves my product or workflows	Kaggle (forums, blog, social media, etc), YouTube (Cloud AI Adventures, Siraj Raval, etc), Podcasts (Chai Time Data Science, Linear Digressions, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc)	Coursera, DataCamp, Kaggle Courses (i.e. Kaggle Learn), Udemy	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	55-59	Female	Germany	Professional degree	NaN	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	40-44	Male	Australia	Master’s degree	Other	> 10,000 employees	20+	I do not know	250,000-299,999	$10,000-$99,999	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 0, -1	1-2 years	Python	Once	2-3 years	NaN	Podcasts (Chai Time Data Science, Linear Digressions, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc), Slack Communities (ods.ai, kagglenoobs, etc)	Coursera, edX, DataCamp, University Courses (resulting in a university degree)	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Visual Studio / Visual Studio Code	Microsoft Azure Notebooks	Python, R, SQL, Bash	Ggplot / ggplot2 , Matplotlib , Seaborn	CPUs, GPUs	Linear or Logistic Regression, Convolutional Neural Networks	Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)	General purpose image/video tools (PIL, cv2, skimage, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	NaN	Scikit-learn , TensorFlow , Keras , RandomForest	Microsoft Azure	Azure Virtual Machines, Azure Container Service	Databricks, Microsoft Analysis Services	Azure Machine Learning Studio	None	Azure SQL Database
4	22-24	Male	India	Bachelor’s degree	Other	0-49 employees	0	No (we do not use ML methods)	4,000-4,999	$0 (USD)	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 1, -1	< 1 years	Python	Never	< 1 years	NaN	YouTube (Cloud AI Adventures, Siraj Raval, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Other	Other	Jupyter (JupyterLab, Jupyter Notebooks, etc)	Google Colab , Google Cloud Notebook Products (AI Platform, Datalab, etc)	Python, SQL	Matplotlib , Plotly / Plotly Express , Seaborn	CPUs, GPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc)	None	NaN	NaN	Scikit-learn , RandomForest, Xgboost , LightGBM	NaN	NaN	NaN	NaN	NaN	NaN
5	50-54	Male	France	Master’s degree	Data Scientist	0-49 employees	3-4	We have well established ML methods (i.e., models in production for more than 2 years)	60,000-69,999	$10,000-$99,999	Advanced statistical software (SPSS, SAS, etc.), -1, 0, -1, -1, -1	20+ years	Java	Never	10-15 years	Build prototypes to explore applying machine learning to new areas, Do research that advances the state of the art of machine learning	YouTube (Cloud AI Adventures, Siraj Raval, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)	None	RStudio , Other	None	Python, R	Ggplot / ggplot2	CPUs, GPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc), Bayesian Approaches, Convolutional Neural Networks, Generative Adversarial Networks, Recurrent Neural Networks	Automated model selection (e.g. auto-sklearn, xcessiv), Automated hyperparameter tuning (e.g. hyperopt, ray.tune), Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)	None	Word embeddings/vectors (GLoVe, fastText, word2vec), Encoder-decorder models (seq2seq, vanilla transformers)	Scikit-learn , TensorFlow , Keras , RandomForest, Xgboost , Caret	Amazon Web Services (AWS)	AWS Elastic Compute Cloud (EC2)	AWS Elastic MapReduce	RapidMiner	Auto-Keras	PostgresSQL, AWS Relational Database Service
6	22-24	Male	India	Master’s degree	Data Scientist	50-249 employees	20+	We are exploring ML methods (and may one day put a model into production)	10,000-14,999	$100-$999	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 2, -1	3-5 years	Python	6-24 times	2-3 years	Analyze and understand data to influence product or business decisions, Experimentation and iteration to improve existing ML models, Do research that advances the state of the art of machine learning	Kaggle (forums, blog, social media, etc), Course Forums (forums.fast.ai, etc), YouTube (Cloud AI Adventures, Siraj Raval, etc), Podcasts (Chai Time Data Science, Linear Digressions, etc), Journal Publications (traditional publications, preprint journals, etc)	Udacity, Coursera, edX, Kaggle Courses (i.e. Kaggle Learn), Udemy	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Spyder , Notepad++ , Sublime Text	Kaggle Notebooks (Kernels) , Google Colab , Binder / JupyterHub	Python, R, Bash	Matplotlib , Plotly / Plotly Express , Bokeh , Seaborn	CPUs, GPUs	Linear or Logistic Regression, Dense Neural Networks (MLPs, etc), Convolutional Neural Networks, Recurrent Neural Networks	Automated data augmentation (e.g. imgaug, albumentations), Automated feature engineering/selection (e.g. tpot, boruta_py), Automated model selection (e.g. auto-sklearn, xcessiv), Automation of full ML pipelines (e.g. Google AutoML, H20 Driverless AI)	General purpose image/video tools (PIL, cv2, skimage, etc), Image segmentation methods (U-Net, Mask R-CNN, etc), Object detection methods (YOLOv3, RetinaNet, etc)	Word embeddings/vectors (GLoVe, fastText, word2vec), Encoder-decorder models (seq2seq, vanilla transformers)	Scikit-learn , TensorFlow , Keras , PyTorch	Google Cloud Platform (GCP) , Amazon Web Services (AWS) , Microsoft Azure	Google Compute Engine (GCE), AWS Lambda, Azure Virtual Machines	Google BigQuery, Databricks	SAS, Azure Machine Learning Studio, Google Cloud Machine Learning Engine	Google AutoML , Tpot , Auto-Keras , Auto-Sklearn , Auto_ml	MySQL, PostgresSQL
7	22-24	Female	United States of America	Bachelor’s degree	Data Scientist	> 10,000 employees	20+	We recently started using ML methods (i.e., models in production for less than 2 years)	80,000-89,999	$0 (USD)	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 3, -1	3-5 years	Python	Once	3-4 years	Analyze and understand data to influence product or business decisions, Build prototypes to explore applying machine learning to new areas, Build and/or run a machine learning service that operationally improves my product or workflows	Hacker News (https://news.ycombinator.com/), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)	Udemy, University Courses (resulting in a university degree)	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Spyder	Microsoft Azure Notebooks , AWS Notebook Products (EMR Notebooks, Sagemaker Notebooks, etc)	Python	Matplotlib , Plotly / Plotly Express	CPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Convolutional Neural Networks	None	General purpose image/video tools (PIL, cv2, skimage, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	NaN	Scikit-learn , TensorFlow , Keras , Spark MLib	NaN	NaN	NaN	NaN	NaN	NaN
8	22-24	Male	United States of America	Bachelor’s degree	Student	NaN	NaN	NaN	NaN	NaN	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 4, -1	3-5 years	Python	Never	1-2 years	NaN	Kaggle (forums, blog, social media, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)	Kaggle Courses (i.e. Kaggle Learn), University Courses (resulting in a university degree)	Jupyter (JupyterLab, Jupyter Notebooks, etc) , PyCharm , Atom	Google Colab	Python	Matplotlib , Seaborn	CPUs, GPUs	Linear or Logistic Regression, Decision Trees or Random Forests, Gradient Boosting Machines (xgboost, lightgbm, etc), Bayesian Approaches, Evolutionary Approaches, Dense Neural Networks (MLPs, etc), Convolutional Neural Networks	None	General purpose image/video tools (PIL, cv2, skimage, etc), Image classification and other general purpose networks (VGG, Inception, ResNet, ResNeXt, NASNet, EfficientNet, etc)	NaN	Scikit-learn , Xgboost , PyTorch , LightGBM	NaN	NaN	NaN	NaN	NaN	NaN
9	55-59	Male	Netherlands	Master’s degree	Other	0-49 employees	1-2	We are exploring ML methods (and may one day put a model into production)	$0-999	$100-$999	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 5, -1	5-10 years	Python	Never	< 1 years	Other	Kaggle (forums, blog, social media, etc), Course Forums (forums.fast.ai, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc)	Coursera	Jupyter (JupyterLab, Jupyter Notebooks, etc)	None	Python, SQL	Matplotlib , D3.js , Seaborn	CPUs	Linear or Logistic Regression, Bayesian Approaches, Generative Adversarial Networks	None	None	NaN	Scikit-learn , PyTorch	None	None	None	None	None	MySQL

	what_is_your_age_#_years	what_is_your_gender	in_which_country_do_you_currently_reside	what_is_the_highest_level_of_formal_education_that_you_have_attained_or_plan_to_attain_within_the_next_2_years	select_the_title_most_similar_to_your_current_role_or_most_recent_title_if_retired	what_is_the_size_of_the_company_where_you_are_employed	approximately_how_many_individuals_are_responsible_for_data_science_workloads_at_your_place_of_business	does_your_current_employer_incorporate_machine_learning_methods_into_their_business	what_is_your_current_yearly_compensation_approximate_$_usd	approximately_how_much_money_have_you_spent_on_machine_learning_and_or_cloud_computing_products_at_your_work_in_the_past_5_years	what_is_the_primary_tool_that_you_use_at_work_or_school_to_analyze_data	how_long_have_you_been_writing_code_to_analyze_data_at_work_or_at_school	what_programming_language_would_you_recommend_an_aspiring_data_scientist_to_learn_first	have_you_ever_used_a_tpu_tensor_processing_unit	for_how_many_years_have_you_used_machine_learning_methods	select_any_activities_that_make_up_an_important_part_of_your_role_at_work	who_what_are_your_favorite_media_sources_that_report_on_data_science_topics	on_which_platforms_have_you_begun_or_completed_data_science_courses	which_of_the_following_integrated_development_environments_id_es_do_you_use_on_a_regular_basis	which_of_the_following_hosted_notebook_products_do_you_use_on_a_regular_basis	what_programming_languages_do_you_use_on_a_regular_basis	what_data_visualization_libraries_or_tools_do_you_use_on_a_regular_basis	which_types_of_specialized_hardware_do_you_use_on_a_regular_basis	which_of_the_following_ml_algorithms_do_you_use_on_a_regular_basis	which_categories_of_ml_tools_do_you_use_on_a_regular_basis	which_categories_of_computer_vision_methods_do_you_use_on_a_regular_basis	which_of_the_following_natural_language_processing_nlp_methods_do_you_use_on_a_regular_basis	which_of_the_following_machine_learning_frameworks_do_you_use_on_a_regular_basis	which_of_the_following_cloud_computing_platforms_do_you_use_on_a_regular_basis	which_specific_cloud_computing_products_do_you_use_on_a_regular_basis	which_specific_big_data_analytics_products_do_you_use_on_a_regular_basis	which_of_the_following_machine_learning_products_do_you_use_on_a_regular_basis	which_automated_machine_learning_tools_or_partial_auto_ml_tools_do_you_use_on_a_regular_basis	which_of_the_following_relational_database_products_do_you_use_on_a_regular_basis
19707	18-21	Male	Viet Nam	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19708	25-29	Female	India	Professional degree	Not employed	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc)	Coursera, DataCamp, Kaggle Courses (i.e. Kaggle Learn), LinkedIn Learning, University Courses (resulting in a university degree)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19709	25-29	Prefer not to say	Austria	No formal education past high school	Data Scientist	250-999 employees	1-2	We use ML methods for generating insights (but do not put working models into production)	1,000-1,999	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	Analyze and understand data to influence product or business decisions	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19710	22-24	Male	India	Bachelor’s degree	Data Scientist	50-249 employees	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19711	18-21	Male	India	Master’s degree	Student	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	Kaggle (forums, blog, social media, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)	Coursera	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19712	50-54	Male	Japan	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19713	18-21	Male	India	Bachelor’s degree	Other	250-999 employees	3-4	I do not know	$0-999	$0 (USD)	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 28, -1	1-2 years	NaN	NaN	NaN	NaN	Reddit (r/machinelearning, r/datascience, etc)	DataCamp, Udemy	Jupyter (JupyterLab, Jupyter Notebooks, etc) , RStudio , PyCharm , Visual Studio / Visual Studio Code , Spyder , Notepad++ , Sublime Text	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19714	35-39	Male	India	Master’s degree	Student	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	Kaggle (forums, blog, social media, etc), Course Forums (forums.fast.ai, etc), YouTube (Cloud AI Adventures, Siraj Raval, etc), Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc), Journal Publications (traditional publications, preprint journals, etc)	Coursera, Kaggle Courses (i.e. Kaggle Learn)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19715	25-29	Male	India	Master’s degree	Statistician	50-249 employees	15-19	We recently started using ML methods (i.e., models in production for less than 2 years)	1,000-1,999	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	Other	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19716	50-54	Male	France	Bachelor’s degree	Software Engineer	> 10,000 employees	20+	We have well established ML methods (i.e., models in production for more than 2 years)	60,000-69,999	$0 (USD)	Local development environments (RStudio, JupyterLab, etc.), -1, -1, -1, 25, -1	3-5 years	Python	Never	4-5 years	Build and/or run the data infrastructure that my business uses for storing, analyzing, and operationalizing data, Build prototypes to explore applying machine learning to new areas	Blogs (Towards Data Science, Medium, Analytics Vidhya, KDnuggets etc)	Coursera, edX, Udemy	Jupyter (JupyterLab, Jupyter Notebooks, etc) , Visual Studio / Visual Studio Code	IBM Watson Studio	Python, SQL, Java, Bash	Matplotlib	CPUs	Linear or Logistic Regression, Decision Trees or Random Forests	Automated model selection (e.g. auto-sklearn, xcessiv), Automated hyperparameter tuning (e.g. hyperopt, ray.tune)	NaN	NaN	Scikit-learn , Spark MLib	NaN	NaN	NaN	NaN	NaN	NaN

Duplicate rows

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	what_is_your_age_#_years	what_is_your_gender	in_which_country_do_you_currently_reside	what_is_the_highest_level_of_formal_education_that_you_have_attained_or_plan_to_attain_within_the_next_2_years	select_the_title_most_similar_to_your_current_role_or_most_recent_title_if_retired	what_is_the_size_of_the_company_where_you_are_employed	approximately_how_many_individuals_are_responsible_for_data_science_workloads_at_your_place_of_business	does_your_current_employer_incorporate_machine_learning_methods_into_their_business	what_is_your_current_yearly_compensation_approximate_$_usd	approximately_how_much_money_have_you_spent_on_machine_learning_and_or_cloud_computing_products_at_your_work_in_the_past_5_years	what_is_the_primary_tool_that_you_use_at_work_or_school_to_analyze_data	how_long_have_you_been_writing_code_to_analyze_data_at_work_or_at_school	what_programming_language_would_you_recommend_an_aspiring_data_scientist_to_learn_first	have_you_ever_used_a_tpu_tensor_processing_unit	for_how_many_years_have_you_used_machine_learning_methods	select_any_activities_that_make_up_an_important_part_of_your_role_at_work	who_what_are_your_favorite_media_sources_that_report_on_data_science_topics	on_which_platforms_have_you_begun_or_completed_data_science_courses	which_of_the_following_integrated_development_environments_id_es_do_you_use_on_a_regular_basis	which_of_the_following_hosted_notebook_products_do_you_use_on_a_regular_basis	what_programming_languages_do_you_use_on_a_regular_basis	what_data_visualization_libraries_or_tools_do_you_use_on_a_regular_basis	which_types_of_specialized_hardware_do_you_use_on_a_regular_basis	which_of_the_following_ml_algorithms_do_you_use_on_a_regular_basis	which_categories_of_ml_tools_do_you_use_on_a_regular_basis	which_categories_of_computer_vision_methods_do_you_use_on_a_regular_basis	which_of_the_following_natural_language_processing_nlp_methods_do_you_use_on_a_regular_basis	which_of_the_following_machine_learning_frameworks_do_you_use_on_a_regular_basis	which_of_the_following_cloud_computing_platforms_do_you_use_on_a_regular_basis	which_specific_cloud_computing_products_do_you_use_on_a_regular_basis	which_specific_big_data_analytics_products_do_you_use_on_a_regular_basis	which_of_the_following_machine_learning_products_do_you_use_on_a_regular_basis	which_automated_machine_learning_tools_or_partial_auto_ml_tools_do_you_use_on_a_regular_basis	which_of_the_following_relational_database_products_do_you_use_on_a_regular_basis	# duplicates
27	18-21	Male	India	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	30
74	22-24	Male	India	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	17
20	18-21	Male	India	Bachelor’s degree	Student	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	12
55	22-24	Male	China	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	12
118	25-29	Male	United States of America	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	10
132	30-34	Male	Japan	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	10
21	18-21	Male	India	Bachelor’s degree	NaN	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	9
67	22-24	Male	India	Bachelor’s degree	NaN	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	8
53	22-24	Male	China	Master’s degree	Student	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7
99	25-29	Male	China	NaN	NaN	NaN	NaN	NaN	NaN	NaN	-1, -1, -1, -1, -1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7

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